diff --git a/.devops/cann.Dockerfile b/.devops/cann.Dockerfile index cd8f87b2ea..83182c9700 100644 --- a/.devops/cann.Dockerfile +++ b/.devops/cann.Dockerfile @@ -4,7 +4,7 @@ # Define the CANN base image for easier version updates later ARG CHIP_TYPE=910b -ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.3.rc1.alpha001-${CHIP_TYPE}-openeuler22.03-py3.11 +ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.3.rc2-${CHIP_TYPE}-openeuler24.03-py3.11 # ============================================================================== # BUILD STAGE @@ -111,7 +111,7 @@ ENTRYPOINT ["/app/tools.sh"] # ============================================================================== FROM base AS light -COPY --from=build /app/full/llama-cli /app +COPY --from=build /app/full/llama-cli /app/full/llama-completion /app ENTRYPOINT [ "/app/llama-cli" ] diff --git a/.devops/cpu.Dockerfile b/.devops/cpu.Dockerfile index 6e16ecda44..b9e84ab986 100644 --- a/.devops/cpu.Dockerfile +++ b/.devops/cpu.Dockerfile @@ -68,7 +68,7 @@ ENTRYPOINT ["/app/tools.sh"] ### Light, CLI only FROM base AS light -COPY --from=build /app/full/llama-cli /app +COPY --from=build /app/full/llama-cli /app/full/llama-completion /app WORKDIR /app diff --git a/.devops/cuda.Dockerfile b/.devops/cuda.Dockerfile index 54f793d0a3..fed5863157 100644 --- a/.devops/cuda.Dockerfile +++ b/.devops/cuda.Dockerfile @@ -74,7 +74,7 @@ ENTRYPOINT ["/app/tools.sh"] ### Light, CLI only FROM base AS light -COPY --from=build /app/full/llama-cli /app +COPY --from=build /app/full/llama-cli /app/full/llama-completion /app WORKDIR /app diff --git a/.devops/intel.Dockerfile b/.devops/intel.Dockerfile index d1a8fbed4c..adebf08229 100644 --- a/.devops/intel.Dockerfile +++ b/.devops/intel.Dockerfile @@ -73,7 +73,7 @@ ENTRYPOINT ["/app/tools.sh"] FROM base AS light COPY --from=build /app/lib/ /app -COPY --from=build /app/full/llama-cli /app +COPY --from=build /app/full/llama-cli /app/full/llama-completion /app WORKDIR /app diff --git a/.devops/musa.Dockerfile b/.devops/musa.Dockerfile index faa3500e61..34d6ad9f40 100644 --- a/.devops/musa.Dockerfile +++ b/.devops/musa.Dockerfile @@ -81,7 +81,7 @@ ENTRYPOINT ["/app/tools.sh"] ### Light, CLI only FROM base AS light -COPY --from=build /app/full/llama-cli /app +COPY --from=build /app/full/llama-cli /app/full/llama-completion /app WORKDIR /app diff --git a/.devops/rocm.Dockerfile b/.devops/rocm.Dockerfile index d6bf28b105..53c3ed8d88 100644 --- a/.devops/rocm.Dockerfile +++ b/.devops/rocm.Dockerfile @@ -94,7 +94,7 @@ ENTRYPOINT ["/app/tools.sh"] ### Light, CLI only FROM base AS light -COPY --from=build /app/full/llama-cli /app +COPY --from=build /app/full/llama-cli /app/full/llama-completion /app WORKDIR /app diff --git a/.devops/s390x.Dockerfile b/.devops/s390x.Dockerfile index b7c9457680..1e66f061d5 100644 --- a/.devops/s390x.Dockerfile +++ b/.devops/s390x.Dockerfile @@ -105,7 +105,7 @@ WORKDIR /llama.cpp/bin # Copy llama.cpp binaries and libraries COPY --from=collector /llama.cpp/bin/*.so /llama.cpp/bin -COPY --from=collector /llama.cpp/bin/llama-cli /llama.cpp/bin +COPY --from=collector /llama.cpp/bin/llama-cli /llama.cpp/bin/llama-completion /llama.cpp/bin ENTRYPOINT [ "/llama.cpp/bin/llama-cli" ] diff --git a/.devops/tools.sh b/.devops/tools.sh index 8a3a693400..cc5ee17dfd 100755 --- a/.devops/tools.sh +++ b/.devops/tools.sh @@ -13,6 +13,8 @@ elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then exec ./llama-quantize "$@" elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then exec ./llama-cli "$@" +elif [[ "$arg1" == '--run-legacy' || "$arg1" == '-l' ]]; then + exec ./llama-completion "$@" elif [[ "$arg1" == '--bench' || "$arg1" == '-b' ]]; then exec ./llama-bench "$@" elif [[ "$arg1" == '--perplexity' || "$arg1" == '-p' ]]; then @@ -32,8 +34,10 @@ elif [[ "$arg1" == '--server' || "$arg1" == '-s' ]]; then else echo "Unknown command: $arg1" echo "Available commands: " - echo " --run (-r): Run a model previously converted into ggml" - echo " ex: -m /models/7B/ggml-model-q4_0.bin -p \"Building a website can be done in 10 simple steps:\" -n 512" + echo " --run (-r): Run a model (chat) previously converted into ggml" + echo " ex: -m /models/7B/ggml-model-q4_0.bin" + echo " --run-legacy (-l): Run a model (legacy completion) previously converted into ggml" + echo " ex: -m /models/7B/ggml-model-q4_0.bin -no-cnv -p \"Building a website can be done in 10 simple steps:\" -n 512" echo " --bench (-b): Benchmark the performance of the inference for various parameters." echo " ex: -m model.gguf" echo " --perplexity (-p): Measure the perplexity of a model over a given text." diff --git a/.devops/vulkan.Dockerfile b/.devops/vulkan.Dockerfile index fd7195c5be..b37b4f277d 100644 --- a/.devops/vulkan.Dockerfile +++ b/.devops/vulkan.Dockerfile @@ -68,7 +68,7 @@ ENTRYPOINT ["/app/tools.sh"] ### Light, CLI only FROM base AS light -COPY --from=build /app/full/llama-cli /app +COPY --from=build /app/full/llama-cli /app/full/llama-completion /app WORKDIR /app diff --git a/.github/ISSUE_TEMPLATE/011-bug-results.yml b/.github/ISSUE_TEMPLATE/011-bug-results.yml index c42a14ff83..b815e70a8d 100644 --- a/.github/ISSUE_TEMPLATE/011-bug-results.yml +++ b/.github/ISSUE_TEMPLATE/011-bug-results.yml @@ -11,7 +11,7 @@ body: (i.e. the generated text) are incorrect or llama.cpp crashes during model evaluation. If you encountered the issue while using an external UI (e.g. ollama), please reproduce your issue using one of the examples/binaries in this repository. - The `llama-cli` binary can be used for simple and reproducible model inference. + The `llama-completion` binary can be used for simple and reproducible model inference. - type: textarea id: version attributes: @@ -74,9 +74,12 @@ body: Please give us a summary of the problem and tell us how to reproduce it. If you can narrow down the bug to specific hardware, compile flags, or command line arguments, that information would be very much appreciated by us. + + If possible, please try to reproduce the issue using `llama-completion` with `-fit off`. + If you can only reproduce the issue with `-fit on`, please provide logs both with and without `--verbose`. placeholder: > - e.g. when I run llama-cli with -ngl 99 I get garbled outputs. - When I use -ngl 0 it works correctly. + e.g. when I run llama-completion with `-fa on` I get garbled outputs for very long prompts. + With short prompts or `-fa off` it works correctly. Here are the exact commands that I used: ... validations: required: true diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index ad205f3ec9..af4c60be64 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -20,7 +20,8 @@ on: '**/*.swift', '**/*.m', '**/*.metal', - '**/*.comp' + '**/*.comp', + '**/*.glsl' ] pull_request: @@ -40,7 +41,8 @@ on: '**/*.swift', '**/*.m', '**/*.metal', - '**/*.comp' + '**/*.comp', + '**/*.glsl' ] concurrency: @@ -243,7 +245,7 @@ jobs: echo "Fetch llama2c model" wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/stories260K.bin ./bin/llama-convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf - ./bin/llama-cli -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256 + ./bin/llama-completion -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256 - name: Test llama2c (s390x) id: llama2c_test_s390x @@ -252,7 +254,7 @@ jobs: cd build echo "Fetch llama2c big-endian model" wget https://huggingface.co/ggml-org/models/resolve/main/tinyllamas/stories260K-be.gguf - ./bin/llama-cli -m stories260K-be.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256 + ./bin/llama-completion -m stories260K-be.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256 ubuntu-latest-cmake-sanitizer: runs-on: ubuntu-latest @@ -1400,25 +1402,54 @@ jobs: chip_type: ['910b', '310p'] build: ['Release'] runs-on: ${{ matrix.arch == 'aarch64' && 'ubuntu-24.04-arm' || 'ubuntu-24.04' }} - container: ascendai/cann:${{ matrix.chip_type == '910b' && '8.3.rc1.alpha001-910b-openeuler22.03-py3.11' || '8.2.rc1-310p-openeuler22.03-py3.11' }} steps: - name: Checkout uses: actions/checkout@v4 + with: + fetch-depth: 0 - - name: Dependencies + - name: Free up disk space + uses: ggml-org/free-disk-space@v1.3.1 + with: + tool-cache: true + + - name: Set container image + id: cann-image run: | - yum update -y - yum install -y git gcc gcc-c++ make cmake libcurl-devel + image="ascendai/cann:${{ matrix.chip_type == '910b' && '8.3.rc2-910b-openeuler24.03-py3.11' || '8.3.rc2-310p-openeuler24.03-py3.11' }}" + echo "image=${image}" >> "${GITHUB_OUTPUT}" + + - name: Pull container image + run: docker pull "${{ steps.cann-image.outputs.image }}" - name: Build + env: + BUILD_TYPE: ${{ matrix.build }} + SOC_TYPE: ascend${{ matrix.chip_type }} run: | - export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH} + HOST_UID=$(id -u) + HOST_GID=$(id -g) - cmake -S . -B build \ - -DCMAKE_BUILD_TYPE=${{ matrix.build }} \ - -DGGML_CANN=on \ - -DSOC_TYPE=ascend${{ matrix.chip_type }} - cmake --build build -j $(nproc) + docker run --rm \ + -v "${PWD}:/workspace" \ + -w /workspace \ + -e SOC_TYPE=${SOC_TYPE} \ + -e BUILD_TYPE=${BUILD_TYPE} \ + "${{ steps.cann-image.outputs.image }}" \ + bash -lc ' + set -e + yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake libcurl-devel + yum clean all && rm -rf /var/cache/yum + git config --global --add safe.directory "/workspace" + export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH} + cmake -S . -B build \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ + -DGGML_CANN=on \ + -DSOC_TYPE=${SOC_TYPE} + cmake --build build -j $(nproc) + + chown -R '"${HOST_UID}"':'"${HOST_GID}"' /workspace/build + ' # TODO: simplify the following workflows using a matrix # TODO: run lighter CI on PRs and the full CI only on master (if needed) @@ -1770,7 +1801,7 @@ jobs: echo "Fetch llama2c model" wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/stories260K.bin ./bin/llama-convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf - ./bin/llama-cli -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256 + ./bin/llama-completion -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256 ubuntu-cmake-sanitizer-riscv64-native: runs-on: RISCV64 diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 77aec20c11..446cae9f84 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -731,6 +731,78 @@ jobs: path: llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz name: llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz + + openEuler-cann: + strategy: + matrix: + arch: [x86, aarch64] + chip_type: ['910b', '310p'] + build: ['Release'] + runs-on: ${{ matrix.arch == 'aarch64' && 'ubuntu-24.04-arm' || 'ubuntu-24.04' }} + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Free up disk space + uses: ggml-org/free-disk-space@v1.3.1 + with: + tool-cache: true + + - name: Set container image + id: cann-image + run: | + image="ascendai/cann:${{ matrix.chip_type == '910b' && '8.3.rc2-910b-openeuler24.03-py3.11' || '8.3.rc2-310p-openeuler24.03-py3.11' }}" + echo "image=${image}" >> "${GITHUB_OUTPUT}" + + - name: Pull container image + run: docker pull "${{ steps.cann-image.outputs.image }}" + + - name: Build + env: + BUILD_TYPE: ${{ matrix.build }} + SOC_TYPE: ascend${{ matrix.chip_type }} + run: | + HOST_UID=$(id -u) + HOST_GID=$(id -g) + + docker run --rm \ + -v "${PWD}:/workspace" \ + -w /workspace \ + -e SOC_TYPE=${SOC_TYPE} \ + -e BUILD_TYPE=${BUILD_TYPE} \ + "${{ steps.cann-image.outputs.image }}" \ + bash -lc ' + set -e + yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake libcurl-devel + yum clean all && rm -rf /var/cache/yum + git config --global --add safe.directory "/workspace" + export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH} + cmake -S . -B build \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ + -DGGML_CANN=on \ + -DSOC_TYPE=${SOC_TYPE} + cmake --build build -j $(nproc) + + chown -R '"${HOST_UID}"':'"${HOST_GID}"' /workspace/build + ' + + - name: Determine tag name + id: tag + uses: ./.github/actions/get-tag-name + + - name: Pack artifacts + run: | + cp LICENSE ./build/bin/ + tar -czvf llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin . + + - name: Upload artifacts (tar) + uses: actions/upload-artifact@v4 + with: + path: llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}.tar.gz + name: llama-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}.tar.gz + release: if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} @@ -752,6 +824,7 @@ jobs: - macOS-arm64 - macOS-x64 - ios-xcode-build + - openEuler-cann steps: - name: Clone @@ -844,6 +917,12 @@ jobs: - [Windows x64 (SYCL)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip) - [Windows x64 (HIP)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-hip-radeon-x64.zip) + **openEuler:** + - [openEuler x86 (310p)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-310p-openEuler-x86.tar.gz) + - [openEuler x86 (910b)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-910b-openEuler-x86.tar.gz) + - [openEuler aarch64 (310p)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-310p-openEuler-aarch64.tar.gz) + - [openEuler aarch64 (910b)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-910b-openEuler-aarch64.tar.gz) + - name: Upload release id: upload_release uses: actions/github-script@v3 diff --git a/.gitignore b/.gitignore index 428f084110..05eb578a82 100644 --- a/.gitignore +++ b/.gitignore @@ -54,6 +54,7 @@ /out/ /tmp/ /autogen-*.md +/common/build-info.cpp # Deprecated diff --git a/CODEOWNERS b/CODEOWNERS index 8e62a36e81..8a0c98c968 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -87,7 +87,8 @@ /tests/ @ggerganov /tests/test-chat-.* @pwilkin /tools/batched-bench/ @ggerganov -/tools/main/ @ggerganov +/tools/cli/ @ngxson +/tools/completion/ @ggerganov /tools/mtmd/ @ngxson /tools/perplexity/ @ggerganov /tools/quantize/ @ggerganov diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index e4f05258db..4545ff8f9a 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -15,6 +15,7 @@ The project differentiates between 3 levels of contributors: - If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends) - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops` - Create separate PRs for each feature or fix. Avoid combining unrelated changes in a single PR +- When adding support for a new model or feature, focus on **CPU support only** in the initial PR unless you have a good reason not to. Add support for other backends like CUDA in follow-up PRs - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly - If your PR becomes stale, rebase it on top of latest `master` to get maintainers attention - Maintainers will rely on your insights and approval when making a final decision to approve and merge a PR diff --git a/README.md b/README.md index 7dd2bfd8a1..5f2076d0a3 100644 --- a/README.md +++ b/README.md @@ -313,7 +313,7 @@ The Hugging Face platform provides a variety of online tools for converting, qua To learn more about model quantization, [read this documentation](tools/quantize/README.md) -## [`llama-cli`](tools/main) +## [`llama-cli`](tools/cli) #### A CLI tool for accessing and experimenting with most of `llama.cpp`'s functionality. @@ -347,19 +347,6 @@ To learn more about model quantization, [read this documentation](tools/quantize --
- Run simple text completion - - To disable conversation mode explicitly, use `-no-cnv` - - ```bash - llama-cli -m model.gguf -p "I believe the meaning of life is" -n 128 -no-cnv - - # I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey. - ``` - -
- -
Constrain the output with a custom grammar @@ -538,7 +525,8 @@ To learn more about model quantization, [read this documentation](tools/quantize ## Other documentation -- [main (cli)](tools/main/README.md) +- [cli](tools/cli/README.md) +- [completion](tools/completion/README.md) - [server](tools/server/README.md) - [GBNF grammars](grammars/README.md) diff --git a/ci/run.sh b/ci/run.sh index 83b2603e82..0a4a0e41eb 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -398,18 +398,20 @@ function gg_run_qwen3_0_6b { ./bin/llama-quantize ${model_bf16} ${model_q5_k} q5_k $(nproc) ./bin/llama-quantize ${model_bf16} ${model_q6_k} q6_k $(nproc) - (time ./bin/llama-cli -no-cnv --model ${model_f16} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/llama-cli -no-cnv --model ${model_bf16} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-bf16.log - (time ./bin/llama-cli -no-cnv --model ${model_q8_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/llama-cli -no-cnv --model ${model_q4_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/llama-cli -no-cnv --model ${model_q4_1} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/llama-cli -no-cnv --model ${model_q5_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/llama-cli -no-cnv --model ${model_q5_1} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/llama-cli -no-cnv --model ${model_q2_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/llama-cli -no-cnv --model ${model_q3_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/llama-cli -no-cnv --model ${model_q4_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/llama-cli -no-cnv --model ${model_q5_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/llama-cli -no-cnv --model ${model_q6_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/llama-fit-params --model ${model_f16} 2>&1 | tee -a $OUT/${ci}-fp-f16.log) + + (time ./bin/llama-completion -no-cnv --model ${model_f16} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-completion -no-cnv --model ${model_bf16} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-bf16.log + (time ./bin/llama-completion -no-cnv --model ${model_q8_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-completion -no-cnv --model ${model_q4_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-completion -no-cnv --model ${model_q4_1} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-completion -no-cnv --model ${model_q5_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-completion -no-cnv --model ${model_q5_1} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-completion -no-cnv --model ${model_q2_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-completion -no-cnv --model ${model_q3_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-completion -no-cnv --model ${model_q4_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-completion -no-cnv --model ${model_q5_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-completion -no-cnv --model ${model_q6_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log if [ -z ${GG_BUILD_NO_BF16} ]; then @@ -523,6 +525,8 @@ function gg_run_embd_bge_small { ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0 + (time ./bin/llama-fit-params --model ${model_f16} 2>&1 | tee -a $OUT/${ci}-fp-f16.log) + (time ./bin/llama-embedding --model ${model_f16} -p "I believe the meaning of life is" -ngl 99 -c 0 --no-op-offload) 2>&1 | tee -a $OUT/${ci}-tg-f16.log (time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" -ngl 99 -c 0 --no-op-offload) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log @@ -563,6 +567,8 @@ function gg_run_rerank_tiny { model_f16="${path_models}/ggml-model-f16.gguf" + (time ./bin/llama-fit-params --model ${model_f16} 2>&1 | tee -a $OUT/${ci}-fp-f16.log) + # for this model, the SEP token is "" (time ./bin/llama-embedding --model ${model_f16} -p "what is panda?\thi\nwhat is panda?\tit's a bear\nwhat is panda?\tThe giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." -ngl 99 -c 0 --pooling rank --embd-normalize -1 --no-op-offload --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index 377b26846b..0182767c2b 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -73,6 +73,8 @@ add_library(${TARGET} STATIC ngram-cache.h peg-parser.cpp peg-parser.h + preset.cpp + preset.h regex-partial.cpp regex-partial.h sampling.cpp diff --git a/common/arg.cpp b/common/arg.cpp index 4203da4a0a..acf4c8f8a8 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -20,6 +20,7 @@ #include #include +#include #include #include #include @@ -47,10 +48,12 @@ #define LLAMA_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083 using json = nlohmann::ordered_json; +using namespace common_arg_utils; static std::initializer_list mmproj_examples = { LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_SERVER, + LLAMA_EXAMPLE_CLI, }; static std::string read_file(const std::string & fname) { @@ -63,6 +66,15 @@ static std::string read_file(const std::string & fname) { return content; } +static const std::vector & get_common_arg_defs() { + static const std::vector options = [] { + common_params params; + auto ctx = common_params_parser_init(params, LLAMA_EXAMPLE_SERVER, nullptr); + return ctx.options; + }(); + return options; +} + common_arg & common_arg::set_examples(std::initializer_list examples) { this->examples = examples; return *this; @@ -94,6 +106,16 @@ bool common_arg::is_exclude(enum llama_example ex) { bool common_arg::get_value_from_env(std::string & output) const { if (env == nullptr) return false; + if (!args_neg.empty()) { + // for compatibility, we need to check LLAMA_ARG_NO_ env as well + std::string neg_env = env; + string_replace_all(neg_env, "LLAMA_ARG_", "LLAMA_ARG_NO_"); + char * neg_value = std::getenv(neg_env.c_str()); + if (neg_value) { + output = "0"; // falsey + return true; + } + } char * value = std::getenv(env); if (value) { output = value; @@ -103,6 +125,14 @@ bool common_arg::get_value_from_env(std::string & output) const { } bool common_arg::has_value_from_env() const { + if (env != nullptr && !args_neg.empty()) { + // for compatibility, we need to check LLAMA_ARG_NO_ env as well + std::string neg_env = env; + string_replace_all(neg_env, "LLAMA_ARG_", "LLAMA_ARG_NO_"); + if (std::getenv(neg_env.c_str())) { + return true; + } + } return env != nullptr && std::getenv(env); } @@ -133,16 +163,17 @@ static std::vector break_str_into_lines(std::string input, size_t m return result; } -std::string common_arg::to_string() { +std::string common_arg::to_string() const { // params for printing to console const static int n_leading_spaces = 40; const static int n_char_per_line_help = 70; // TODO: detect this based on current console std::string leading_spaces(n_leading_spaces, ' '); std::ostringstream ss; - for (const auto arg : args) { - if (arg == args.front()) { - if (args.size() == 1) { + auto all_args = get_args(); // also contains args_neg + for (const auto & arg : all_args) { + if (arg == all_args.front()) { + if (all_args.size() == 1) { ss << arg; } else { // first arg is usually abbreviation, we need padding to make it more beautiful @@ -151,7 +182,7 @@ std::string common_arg::to_string() { ss << tmp << spaces; } } else { - ss << arg << (arg != args.back() ? ", " : ""); + ss << arg << (arg != all_args.back() ? ", " : ""); } } if (value_hint) ss << " " << value_hint; @@ -170,6 +201,31 @@ std::string common_arg::to_string() { return ss.str(); } +std::vector common_arg::get_args() const { + std::vector result; + for (const auto & arg : args) { + result.push_back(std::string(arg)); + } + for (const auto & arg : args_neg) { + result.push_back(std::string(arg)); + } + return result; +} + +std::vector common_arg::get_env() const { + std::vector result; + if (env) { + result.push_back(std::string(env)); + } + if (!args_neg.empty() && env) { + // for compatibility, we need to add LLAMA_ARG_NO_ variant + std::string neg_env = env; + string_replace_all(neg_env, "LLAMA_ARG_", "LLAMA_ARG_NO_"); + result.push_back(neg_env); + } + return result; +} + // // utils // @@ -305,6 +361,16 @@ static std::string get_all_kv_cache_types() { return msg.str(); } +static bool parse_bool_value(const std::string & value) { + if (is_truthy(value)) { + return true; + } else if (is_falsey(value)) { + return false; + } else { + throw std::invalid_argument("invalid boolean value"); + } +} + // // CLI argument parsing functions // @@ -312,10 +378,13 @@ static std::string get_all_kv_cache_types() { static bool common_params_parse_ex(int argc, char ** argv, common_params_context & ctx_arg) { common_params & params = ctx_arg.params; - std::unordered_map arg_to_options; + std::unordered_map> arg_to_options; for (auto & opt : ctx_arg.options) { for (const auto & arg : opt.args) { - arg_to_options[arg] = &opt; + arg_to_options[arg] = {&opt, /* is_positive */ true}; + } + for (const auto & arg : opt.args_neg) { + arg_to_options[arg] = {&opt, /* is_positive */ false}; } } @@ -324,12 +393,15 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context std::string value; if (opt.get_value_from_env(value)) { try { - if (opt.handler_void && (value == "1" || value == "true")) { + if (opt.handler_void && is_truthy(value)) { opt.handler_void(params); } if (opt.handler_int) { opt.handler_int(params, std::stoi(value)); } + if (opt.handler_bool) { + opt.handler_bool(params, parse_bool_value(value)); + } if (opt.handler_string) { opt.handler_string(params, value); continue; @@ -358,7 +430,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context if (arg_to_options.find(arg) == arg_to_options.end()) { throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str())); } - auto opt = *arg_to_options[arg]; + auto & tmp = arg_to_options[arg]; + auto opt = *tmp.first; + bool is_positive = tmp.second; if (opt.has_value_from_env()) { fprintf(stderr, "warn: %s environment variable is set, but will be overwritten by command line argument %s\n", opt.env, arg.c_str()); } @@ -367,6 +441,10 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context opt.handler_void(params); continue; } + if (opt.handler_bool) { + opt.handler_bool(params, is_positive); + continue; + } // arg with single value check_arg(i); @@ -391,7 +469,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context throw std::invalid_argument(string_format( "error while handling argument \"%s\": %s\n\n" "usage:\n%s\n\nto show complete usage, run with -h", - arg.c_str(), e.what(), arg_to_options[arg]->to_string().c_str())); + arg.c_str(), e.what(), opt.to_string().c_str())); } } @@ -427,7 +505,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context // model is required (except for server) // TODO @ngxson : maybe show a list of available models in CLI in this case - if (params.model.path.empty() && ctx_arg.ex != LLAMA_EXAMPLE_SERVER && !params.usage) { + if (params.model.path.empty() && ctx_arg.ex != LLAMA_EXAMPLE_SERVER && !params.usage && !params.completion) { throw std::invalid_argument("error: --model is required\n"); } @@ -452,7 +530,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context params.kv_overrides.back().key[0] = 0; } - if (!params.tensor_buft_overrides.empty()) { + // pad tensor_buft_overrides for llama_params_fit: + const size_t ntbo = llama_max_tensor_buft_overrides(); + while (params.tensor_buft_overrides.size() < ntbo) { params.tensor_buft_overrides.push_back({nullptr, nullptr}); } @@ -468,6 +548,8 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context )); } + common_log_set_verbosity_thold(params.verbosity); + return true; } @@ -560,6 +642,7 @@ static void common_params_print_completion(common_params_context & ctx_arg) { "llama-batched-bench", "llama-bench", "llama-cli", + "llama-completion", "llama-convert-llama2c-to-ggml", "llama-cvector-generator", "llama-embedding", @@ -644,6 +727,56 @@ static void add_rpc_devices(const std::string & servers) { } } +bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map & out_map) { + common_params dummy_params; + common_params_context ctx_arg = common_params_parser_init(dummy_params, ex, nullptr); + + std::unordered_map arg_to_options; + for (auto & opt : ctx_arg.options) { + for (const auto & arg : opt.args) { + arg_to_options[arg] = &opt; + } + for (const auto & arg : opt.args_neg) { + arg_to_options[arg] = &opt; + } + } + + // TODO @ngxson : find a way to deduplicate this code + + // handle command line arguments + auto check_arg = [&](int i) { + if (i+1 >= argc) { + throw std::invalid_argument("expected value for argument"); + } + }; + + for (int i = 1; i < argc; i++) { + const std::string arg_prefix = "--"; + + std::string arg = argv[i]; + if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { + std::replace(arg.begin(), arg.end(), '_', '-'); + } + if (arg_to_options.find(arg) == arg_to_options.end()) { + throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str())); + } + auto opt = *arg_to_options[arg]; + std::string val; + if (opt.value_hint != nullptr) { + // arg with single value + check_arg(i); + val = argv[++i]; + } + if (opt.value_hint_2 != nullptr) { + // TODO: support arg with 2 values + throw std::invalid_argument("error: argument with 2 values is not yet supported\n"); + } + out_map[opt] = val; + } + + return true; +} + bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **)) { auto ctx_arg = common_params_parser_init(params, ex, print_usage); const common_params params_org = ctx_arg.params; // the example can modify the default params @@ -689,25 +822,19 @@ static std::string list_builtin_chat_templates() { return msg.str(); } -static bool is_truthy(const std::string & value) { - return value == "on" || value == "enabled" || value == "1"; +bool common_arg_utils::is_truthy(const std::string & value) { + return value == "on" || value == "enabled" || value == "true" || value == "1"; } -static bool is_falsey(const std::string & value) { - return value == "off" || value == "disabled" || value == "0"; +bool common_arg_utils::is_falsey(const std::string & value) { + return value == "off" || value == "disabled" || value == "false" || value == "0"; } -static bool is_autoy(const std::string & value) { +bool common_arg_utils::is_autoy(const std::string & value) { return value == "auto" || value == "-1"; } common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **)) { - // default values specific to example - // note: we place it here instead of inside server.cpp to allow llama-gen-docs to pick it up - if (ex == LLAMA_EXAMPLE_SERVER) { - params.use_jinja = true; - } - params.use_color = tty_can_use_colors(); // load dynamic backends @@ -785,12 +912,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } )); add_opt(common_arg( + {"--display-prompt"}, {"--no-display-prompt"}, - string_format("don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false"), - [](common_params & params) { - params.display_prompt = false; + string_format("whether to print prompt at generation (default: %s)", params.display_prompt ? "true" : "false"), + [](common_params & params, bool value) { + params.display_prompt = value; } - ).set_examples({LLAMA_EXAMPLE_MAIN})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI})); add_opt(common_arg( {"-co", "--color"}, "[on|off|auto]", "Colorize output to distinguish prompt and user input from generations ('on', 'off', or 'auto', default: 'auto')\n" @@ -807,7 +935,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex string_format("error: unknown value for --color: '%s'\n", value.c_str())); } } - ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP})); add_opt(common_arg( {"-t", "--threads"}, "N", string_format("number of CPU threads to use during generation (default: %d)", params.cpuparams.n_threads), @@ -940,7 +1068,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex add_opt(common_arg( {"-n", "--predict", "--n-predict"}, "N", string_format( - ex == LLAMA_EXAMPLE_MAIN + ex == LLAMA_EXAMPLE_COMPLETION ? "number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)" : "number of tokens to predict (default: %d, -1 = infinity)", params.n_predict), @@ -984,7 +1112,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, int value) { params.n_ctx_checkpoints = value; } - ).set_env("LLAMA_ARG_CTX_CHECKPOINTS").set_examples({LLAMA_EXAMPLE_SERVER})); + ).set_env("LLAMA_ARG_CTX_CHECKPOINTS").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); add_opt(common_arg( {"--cache-ram", "-cram"}, "N", string_format("set the maximum cache size in MiB (default: %d, -1 - no limit, 0 - disable)\n" @@ -992,7 +1120,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, int value) { params.cache_ram_mib = value; } - ).set_env("LLAMA_ARG_CACHE_RAM").set_examples({LLAMA_EXAMPLE_SERVER})); + ).set_env("LLAMA_ARG_CACHE_RAM").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); add_opt(common_arg( {"--kv-unified", "-kvu"}, string_format("use single unified KV buffer for the KV cache of all sequences (default: %s)\n" @@ -1001,20 +1129,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.kv_unified = true; } ).set_env("LLAMA_ARG_KV_UNIFIED")); - add_opt(common_arg( - {"--no-context-shift"}, - string_format("disables context shift on infinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"), - [](common_params & params) { - params.ctx_shift = false; - } - ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT")); add_opt(common_arg( {"--context-shift"}, - string_format("enables context shift on infinite text generation (default: %s)", params.ctx_shift ? "enabled" : "disabled"), - [](common_params & params) { - params.ctx_shift = true; + {"--no-context-shift"}, + string_format("whether to use context shift on infinite text generation (default: %s)", params.ctx_shift ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.ctx_shift = value; } - ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_CONTEXT_SHIFT")); + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_CONTEXT_SHIFT")); add_opt(common_arg( {"--chunks"}, "N", string_format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks), @@ -1050,15 +1172,24 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, const std::string & value) { params.system_prompt = value; } - ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_DIFFUSION})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_DIFFUSION})); add_opt(common_arg( + {"--perf"}, {"--no-perf"}, - string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"), - [](common_params & params) { - params.no_perf = true; - params.sampling.no_perf = true; + string_format("whether to enable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"), + [](common_params & params, bool value) { + params.no_perf = !value; + params.sampling.no_perf = !value; } - ).set_env("LLAMA_ARG_NO_PERF")); + ).set_env("LLAMA_ARG_PERF")); + add_opt(common_arg( + {"--show-timings"}, + {"--no-show-timings"}, + string_format("whether to show timing information after each response (default: %s)", params.show_timings ? "true" : "false"), + [](common_params & params, bool value) { + params.show_timings = value; + } + ).set_examples({LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SHOW_TIMINGS")); add_opt(common_arg( {"-f", "--file"}, "FNAME", "a file containing the prompt (default: none)", @@ -1080,7 +1211,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.system_prompt.pop_back(); } } - ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_DIFFUSION})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_DIFFUSION})); add_opt(common_arg( {"--in-file"}, "FNAME", "an input file (repeat to specify multiple files)", @@ -1110,16 +1241,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_excludes({LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( {"-e", "--escape"}, - string_format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"), - [](common_params & params) { - params.escape = true; - } - )); - add_opt(common_arg( {"--no-escape"}, - "do not process escape sequences", - [](common_params & params) { - params.escape = false; + string_format("whether to process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"), + [](common_params & params, bool value) { + params.escape = value; } )); add_opt(common_arg( @@ -1128,59 +1253,53 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, int value) { params.n_print = value; } - ).set_examples({LLAMA_EXAMPLE_MAIN})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION})); add_opt(common_arg( {"--prompt-cache"}, "FNAME", "file to cache prompt state for faster startup (default: none)", [](common_params & params, const std::string & value) { params.path_prompt_cache = value; } - ).set_examples({LLAMA_EXAMPLE_MAIN})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION})); add_opt(common_arg( {"--prompt-cache-all"}, "if specified, saves user input and generations to cache as well\n", [](common_params & params) { params.prompt_cache_all = true; } - ).set_examples({LLAMA_EXAMPLE_MAIN})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION})); add_opt(common_arg( {"--prompt-cache-ro"}, "if specified, uses the prompt cache but does not update it", [](common_params & params) { params.prompt_cache_ro = true; } - ).set_examples({LLAMA_EXAMPLE_MAIN})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION})); add_opt(common_arg( {"-r", "--reverse-prompt"}, "PROMPT", "halt generation at PROMPT, return control in interactive mode\n", [](common_params & params, const std::string & value) { params.antiprompt.emplace_back(value); } - ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( {"-sp", "--special"}, string_format("special tokens output enabled (default: %s)", params.special ? "true" : "false"), [](common_params & params) { params.special = true; } - ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( {"-cnv", "--conversation"}, - "run in conversation mode:\n" + {"-no-cnv", "--no-conversation"}, + "whether to run in conversation mode:\n" "- does not print special tokens and suffix/prefix\n" "- interactive mode is also enabled\n" "(default: auto enabled if chat template is available)", - [](common_params & params) { - params.conversation_mode = COMMON_CONVERSATION_MODE_ENABLED; + [](common_params & params, bool value) { + params.conversation_mode = value ? COMMON_CONVERSATION_MODE_ENABLED : COMMON_CONVERSATION_MODE_DISABLED; } - ).set_examples({LLAMA_EXAMPLE_MAIN})); - add_opt(common_arg( - {"-no-cnv", "--no-conversation"}, - "force disable conversation mode (default: false)", - [](common_params & params) { - params.conversation_mode = COMMON_CONVERSATION_MODE_DISABLED; - } - ).set_examples({LLAMA_EXAMPLE_MAIN})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI})); add_opt(common_arg( {"-st", "--single-turn"}, "run conversation for a single turn only, then exit when done\n" @@ -1189,28 +1308,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params) { params.single_turn = true; } - ).set_examples({LLAMA_EXAMPLE_MAIN})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI})); add_opt(common_arg( {"-i", "--interactive"}, string_format("run in interactive mode (default: %s)", params.interactive ? "true" : "false"), [](common_params & params) { params.interactive = true; } - ).set_examples({LLAMA_EXAMPLE_MAIN})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION})); add_opt(common_arg( {"-if", "--interactive-first"}, string_format("run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false"), [](common_params & params) { params.interactive_first = true; } - ).set_examples({LLAMA_EXAMPLE_MAIN})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION})); add_opt(common_arg( {"-mli", "--multiline-input"}, "allows you to write or paste multiple lines without ending each in '\\'", [](common_params & params) { params.multiline_input = true; } - ).set_examples({LLAMA_EXAMPLE_MAIN})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI})); add_opt(common_arg( {"--in-prefix-bos"}, "prefix BOS to user inputs, preceding the `--in-prefix` string", @@ -1218,7 +1337,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.input_prefix_bos = true; params.enable_chat_template = false; } - ).set_examples({LLAMA_EXAMPLE_MAIN})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION})); add_opt(common_arg( {"--in-prefix"}, "STRING", "string to prefix user inputs with (default: empty)", @@ -1226,7 +1345,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.input_prefix = value; params.enable_chat_template = false; } - ).set_examples({LLAMA_EXAMPLE_MAIN})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION})); add_opt(common_arg( {"--in-suffix"}, "STRING", "string to suffix after user inputs with (default: empty)", @@ -1234,14 +1353,15 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.input_suffix = value; params.enable_chat_template = false; } - ).set_examples({LLAMA_EXAMPLE_MAIN})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION})); add_opt(common_arg( + {"--warmup"}, {"--no-warmup"}, - "skip warming up the model with an empty run", - [](common_params & params) { - params.warmup = false; + string_format("whether to perform warmup with an empty run (default: %s)", params.warmup ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.warmup = value; } - ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_PERPLEXITY})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_PERPLEXITY})); add_opt(common_arg( {"--spm-infill"}, string_format( @@ -1298,7 +1418,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.sampling.top_k = value; params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_K; } - ).set_sparam()); + ).set_sparam().set_env("LLAMA_ARG_TOP_K")); add_opt(common_arg( {"--top-p"}, "N", string_format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sampling.top_p), @@ -1632,28 +1752,30 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, int value) { params.grp_attn_n = value; } - ).set_env("LLAMA_ARG_GRP_ATTN_N").set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_PASSKEY})); + ).set_env("LLAMA_ARG_GRP_ATTN_N").set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_PASSKEY})); add_opt(common_arg( {"-gaw", "--grp-attn-w"}, "N", string_format("group-attention width (default: %d)", params.grp_attn_w), [](common_params & params, int value) { params.grp_attn_w = value; } - ).set_env("LLAMA_ARG_GRP_ATTN_W").set_examples({LLAMA_EXAMPLE_MAIN})); + ).set_env("LLAMA_ARG_GRP_ATTN_W").set_examples({LLAMA_EXAMPLE_COMPLETION})); add_opt(common_arg( + {"-kvo", "--kv-offload"}, {"-nkvo", "--no-kv-offload"}, - "disable KV offload", - [](common_params & params) { - params.no_kv_offload = true; + string_format("whether to enable KV cache offloading (default: %s)", params.no_kv_offload ? "disabled" : "enabled"), + [](common_params & params, bool value) { + params.no_kv_offload = !value; } - ).set_env("LLAMA_ARG_NO_KV_OFFLOAD")); + ).set_env("LLAMA_ARG_KV_OFFLOAD")); add_opt(common_arg( + {"--repack"}, {"-nr", "--no-repack"}, - "disable weight repacking", - [](common_params & params) { - params.no_extra_bufts = true; + string_format("whether to enable weight repacking (default: %s)", params.no_extra_bufts ? "disabled" : "enabled"), + [](common_params & params, bool value) { + params.no_extra_bufts = !value; } - ).set_env("LLAMA_ARG_NO_REPACK")); + ).set_env("LLAMA_ARG_REPACK")); add_opt(common_arg( {"--no-host"}, "bypass host buffer allowing extra buffers to be used", @@ -1782,20 +1904,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_PARALLEL})); add_opt(common_arg( {"-cb", "--cont-batching"}, - string_format("enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"), - [](common_params & params) { - params.cont_batching = true; + {"-nocb", "--no-cont-batching"}, + string_format("whether to enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.cont_batching = value; } ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CONT_BATCHING")); add_opt(common_arg( - {"-nocb", "--no-cont-batching"}, - "disable continuous batching", - [](common_params & params) { - params.cont_batching = false; - } - ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONT_BATCHING")); - add_opt(common_arg( - {"--mmproj"}, "FILE", + {"-mm", "--mmproj"}, "FILE", "path to a multimodal projector file. see tools/mtmd/README.md\n" "note: if -hf is used, this argument can be omitted", [](common_params & params, const std::string & value) { @@ -1803,33 +1919,35 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ")); add_opt(common_arg( - {"--mmproj-url"}, "URL", + {"-mmu", "--mmproj-url"}, "URL", "URL to a multimodal projector file. see tools/mtmd/README.md", [](common_params & params, const std::string & value) { params.mmproj.url = value; } ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_URL")); add_opt(common_arg( - {"--no-mmproj"}, - "explicitly disable multimodal projector, useful when using -hf", - [](common_params & params) { - params.no_mmproj = true; + {"--mmproj-auto"}, + {"--no-mmproj", "--no-mmproj-auto"}, + string_format("whether to use multimodal projector file (if available), useful when using -hf (default: %s)", params.no_mmproj ? "disabled" : "enabled"), + [](common_params & params, bool value) { + params.no_mmproj = !value; } - ).set_examples(mmproj_examples).set_env("LLAMA_ARG_NO_MMPROJ")); + ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_AUTO")); add_opt(common_arg( + {"--mmproj-offload"}, {"--no-mmproj-offload"}, - "do not offload multimodal projector to GPU", - [](common_params & params) { - params.mmproj_use_gpu = false; + string_format("whether to enable GPU offloading for multimodal projector (default: %s)", params.mmproj_use_gpu ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.mmproj_use_gpu = value; } - ).set_examples(mmproj_examples).set_env("LLAMA_ARG_NO_MMPROJ_OFFLOAD")); + ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_OFFLOAD")); add_opt(common_arg( {"--image", "--audio"}, "FILE", "path to an image or audio file. use with multimodal models, can be repeated if you have multiple files\n", [](common_params & params, const std::string & value) { params.image.emplace_back(value); } - ).set_examples({LLAMA_EXAMPLE_MTMD})); + ).set_examples({LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_CLI})); add_opt(common_arg( {"--image-min-tokens"}, "N", "minimum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)", @@ -1862,12 +1980,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_env("LLAMA_ARG_MLOCK")); add_opt(common_arg( + {"--mmap"}, {"--no-mmap"}, - "do not memory-map model (slower load but may reduce pageouts if not using mlock)", - [](common_params & params) { - params.use_mmap = false; + string_format("whether to memory-map model (if disabled, slower load but may reduce pageouts if not using mlock) (default: %s)", params.use_mmap ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.use_mmap = value; } - ).set_env("LLAMA_ARG_NO_MMAP")); + ).set_env("LLAMA_ARG_MMAP")); add_opt(common_arg( {"--numa"}, "TYPE", "attempt optimizations that help on some NUMA systems\n" @@ -1922,7 +2041,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex "override tensor buffer type for draft model", [](common_params & params, const std::string & value) { parse_tensor_buffer_overrides(value, params.speculative.tensor_buft_overrides); } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); add_opt(common_arg( {"--cpu-moe", "-cmoe"}, "keep all Mixture of Experts (MoE) weights in the CPU", @@ -1951,7 +2070,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params) { params.speculative.tensor_buft_overrides.push_back(llm_ffn_exps_cpu_override()); } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CPU_MOE_DRAFT")); + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_CPU_MOE_DRAFT")); add_opt(common_arg( {"--n-cpu-moe-draft", "-ncmoed"}, "N", "keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model", @@ -1965,7 +2084,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.speculative.tensor_buft_overrides.push_back({buft_overrides_draft.back().c_str(), ggml_backend_cpu_buffer_type()}); } } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_N_CPU_MOE_DRAFT")); + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_N_CPU_MOE_DRAFT")); add_opt(common_arg( {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N", string_format("max. number of layers to store in VRAM (default: %d)", params.n_gpu_layers), @@ -2037,6 +2156,34 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } } ).set_env("LLAMA_ARG_MAIN_GPU")); + add_opt(common_arg( + { "-fit", "--fit" }, "[on|off]", + string_format("whether to adjust unset arguments to fit in device memory ('on' or 'off', default: '%s')", params.fit_params ? "on" : "off"), + [](common_params & params, const std::string & value) { + if (is_truthy(value)) { + params.fit_params = true; + } else if (is_falsey(value)) { + params.fit_params = false; + } else { + throw std::runtime_error( + string_format("error: unkown value for --fit: '%s'\n", value.c_str())); + } + } + ).set_env("LLAMA_ARG_FIT")); + add_opt(common_arg( + { "-fitt", "--fit-target" }, "MiB", + string_format("target margin per device for --fit option, default: %zu", params.fit_params_target/(1024*1024)), + [](common_params & params, int value) { + params.fit_params_target = value * size_t(1024*1024); + } + ).set_env("LLAMA_ARG_FIT_TARGET")); + add_opt(common_arg( + { "-fitc", "--fit-ctx" }, "N", + string_format("minimum ctx size that can be set by --fit option, default: %" PRIu32, params.fit_params_min_ctx), + [](common_params & params, int value) { + params.fit_params_min_ctx = value; + } + ).set_env("LLAMA_ARG_FIT_CTX")); add_opt(common_arg( {"--check-tensors"}, string_format("check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false"), @@ -2055,10 +2202,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } )); add_opt(common_arg( + {"--op-offload"}, {"--no-op-offload"}, - string_format("disable offloading host tensor operations to device (default: %s)", params.no_op_offload ? "true" : "false"), - [](common_params & params) { - params.no_op_offload = true; + string_format("whether to offload host tensor operations to device (default: %s)", params.no_op_offload ? "false" : "true"), + [](common_params & params, bool value) { + params.no_op_offload = !value; } )); add_opt(common_arg( @@ -2254,10 +2402,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples({LLAMA_EXAMPLE_IMATRIX})); add_opt(common_arg( + {"--ppl"}, {"--no-ppl"}, - string_format("do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"), - [](common_params & params) { - params.compute_ppl = false; + string_format("whether to compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"), + [](common_params & params, bool value) { + params.compute_ppl = value; } ).set_examples({LLAMA_EXAMPLE_IMATRIX})); add_opt(common_arg( @@ -2376,12 +2525,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_API_PREFIX")); add_opt(common_arg( + {"--webui"}, {"--no-webui"}, - string_format("Disable the Web UI (default: %s)", params.webui ? "enabled" : "disabled"), - [](common_params & params) { - params.webui = false; + string_format("whether to enable the Web UI (default: %s)", params.webui ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.webui = value; } - ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_WEBUI")); + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI")); add_opt(common_arg( {"--embedding", "--embeddings"}, string_format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"), @@ -2444,7 +2594,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.default_template_kwargs[item.key()] = item.value().dump(); } } - ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_CHAT_TEMPLATE_KWARGS")); + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_CHAT_TEMPLATE_KWARGS")); add_opt(common_arg( {"-to", "--timeout"}, "N", string_format("server read/write timeout in seconds (default: %d)", params.timeout_read), @@ -2486,18 +2636,12 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_PROPS")); add_opt(common_arg( {"--slots"}, - string_format("enable slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"), - [](common_params & params) { - params.endpoint_slots = true; + {"--no-slots"}, + string_format("expose slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.endpoint_slots = value; } ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_SLOTS")); - add_opt(common_arg( - {"--no-slots"}, - "disables slots monitoring endpoint", - [](common_params & params) { - params.endpoint_slots = false; - } - ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_ENDPOINT_SLOTS")); add_opt(common_arg( {"--slot-save-path"}, "PATH", "path to save slot kv cache (default: disabled)", @@ -2533,6 +2677,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.models_dir = value; } ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_DIR")); + add_opt(common_arg( + {"--models-preset"}, "PATH", + "path to INI file containing model presets for the router server (default: disabled)", + [](common_params & params, const std::string & value) { + params.models_preset = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_PRESET")); add_opt(common_arg( {"--models-max"}, "N", string_format("for router server, maximum number of models to load simultaneously (default: %d, 0 = unlimited)", params.models_max), @@ -2541,26 +2692,21 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_MAX")); add_opt(common_arg( + {"--models-autoload"}, {"--no-models-autoload"}, - "disables automatic loading of models (default: enabled)", - [](common_params & params) { - params.models_autoload = false; + string_format("for router server, whether to automatically load models (default: %s)", params.models_autoload ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.models_autoload = value; } - ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_MODELS_AUTOLOAD")); + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_AUTOLOAD")); add_opt(common_arg( {"--jinja"}, - string_format("use jinja template for chat (default: %s)\n", params.use_jinja ? "enabled" : "disabled"), - [](common_params & params) { - params.use_jinja = true; - } - ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_JINJA")); - add_opt(common_arg( {"--no-jinja"}, - string_format("disable jinja template for chat (default: %s)\n", params.use_jinja ? "enabled" : "disabled"), - [](common_params & params) { - params.use_jinja = false; + string_format("whether to use jinja template engine for chat (default: %s)", params.use_jinja ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.use_jinja = value; } - ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_NO_JINJA")); + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_JINJA")); add_opt(common_arg( {"--reasoning-format"}, "FORMAT", "controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:\n" @@ -2571,7 +2717,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, const std::string & value) { params.reasoning_format = common_reasoning_format_from_name(value); } - ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_THINK")); + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_THINK")); add_opt(common_arg( {"--reasoning-budget"}, "N", "controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)", @@ -2579,7 +2725,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex if (value != 0 && value != -1) { throw std::invalid_argument("invalid value"); } params.reasoning_budget = value; } - ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_THINK_BUDGET")); + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_THINK_BUDGET")); add_opt(common_arg( {"--chat-template"}, "JINJA_TEMPLATE", string_format( @@ -2591,7 +2737,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, const std::string & value) { params.chat_template = value; } - ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_CHAT_TEMPLATE")); + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_CHAT_TEMPLATE")); add_opt(common_arg( {"--chat-template-file"}, "JINJA_TEMPLATE_FILE", string_format( @@ -2603,17 +2749,18 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, const std::string & value) { params.chat_template = read_file(value); } - ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE")); + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE")); add_opt(common_arg( + {"--prefill-assistant"}, {"--no-prefill-assistant"}, string_format( "whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)\n" "when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled\n" ), - [](common_params & params) { - params.prefill_assistant = false; + [](common_params & params, bool value) { + params.prefill_assistant = value; } - ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_PREFILL_ASSISTANT")); + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_PREFILL_ASSISTANT")); add_opt(common_arg( {"-sps", "--slot-prompt-similarity"}, "SIMILARITY", string_format("how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity), @@ -2634,7 +2781,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params) { params.simple_io = true; } - ).set_examples({LLAMA_EXAMPLE_MAIN})); + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI})); add_opt(common_arg( {"--positive-file"}, "FNAME", string_format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()), @@ -2717,7 +2864,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex "Set verbosity level to infinity (i.e. log all messages, useful for debugging)", [](common_params & params) { params.verbosity = INT_MAX; - common_log_set_verbosity_thold(INT_MAX); } )); add_opt(common_arg( @@ -2738,7 +2884,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex "(default: %d)\n", params.verbosity), [](common_params & params, int value) { params.verbosity = value; - common_log_set_verbosity_thold(value); } ).set_env("LLAMA_LOG_VERBOSITY")); add_opt(common_arg( @@ -2871,14 +3016,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, int value) { params.speculative.n_max = value; } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_MAX")); + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_DRAFT_MAX")); add_opt(common_arg( {"--draft-min", "--draft-n-min"}, "N", string_format("minimum number of draft tokens to use for speculative decoding (default: %d)", params.speculative.n_min), [](common_params & params, int value) { params.speculative.n_min = value; } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_MIN")); + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_DRAFT_MIN")); add_opt(common_arg( {"--draft-p-split"}, "P", string_format("speculative decoding split probability (default: %.1f)", (double)params.speculative.p_split), @@ -2892,14 +3037,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, const std::string & value) { params.speculative.p_min = std::stof(value); } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_P_MIN")); + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_DRAFT_P_MIN")); add_opt(common_arg( {"-cd", "--ctx-size-draft"}, "N", string_format("size of the prompt context for the draft model (default: %d, 0 = loaded from model)", params.speculative.n_ctx), [](common_params & params, int value) { params.speculative.n_ctx = value; } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CTX_SIZE_DRAFT")); + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_CTX_SIZE_DRAFT")); add_opt(common_arg( {"-devd", "--device-draft"}, "", "comma-separated list of devices to use for offloading the draft model (none = don't offload)\n" @@ -2907,7 +3052,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, const std::string & value) { params.speculative.devices = parse_device_list(value); } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); add_opt(common_arg( {"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N", "number of layers to store in VRAM for the draft model", @@ -2919,21 +3064,21 @@ common_params_context common_params_parser_init(common_params & params, llama_ex fprintf(stderr, "warning: consult docs/build.md for compilation instructions\n"); } } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_N_GPU_LAYERS_DRAFT")); + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_N_GPU_LAYERS_DRAFT")); add_opt(common_arg( {"-md", "--model-draft"}, "FNAME", "draft model for speculative decoding (default: unused)", [](common_params & params, const std::string & value) { params.speculative.model.path = value; } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODEL_DRAFT")); + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_MODEL_DRAFT")); add_opt(common_arg( {"--spec-replace"}, "TARGET", "DRAFT", "translate the string in TARGET into DRAFT if the draft model and main model are not compatible", [](common_params & params, const std::string & tgt, const std::string & dft) { params.speculative.replacements.push_back({ tgt, dft }); } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); add_opt(common_arg( {"-ctkd", "--cache-type-k-draft"}, "TYPE", string_format( @@ -3197,7 +3342,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.use_jinja = true; //params.default_template_kwargs["reasoning_effort"] = "\"high\""; } - ).set_examples({LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); add_opt(common_arg( {"--gpt-oss-120b-default"}, @@ -3216,7 +3361,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.use_jinja = true; //params.default_template_kwargs["reasoning_effort"] = "\"high\""; } - ).set_examples({LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); add_opt(common_arg( {"--vision-gemma-4b-default"}, @@ -3227,7 +3372,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.n_ctx = 0; params.use_jinja = true; } - ).set_examples({LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); add_opt(common_arg( {"--vision-gemma-12b-default"}, @@ -3238,7 +3383,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.n_ctx = 0; params.use_jinja = true; } - ).set_examples({LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); return ctx_arg; } diff --git a/common/arg.h b/common/arg.h index 7ab7e2cea4..1321595c1a 100644 --- a/common/arg.h +++ b/common/arg.h @@ -3,8 +3,10 @@ #include "common.h" #include +#include #include #include +#include // // CLI argument parsing @@ -14,6 +16,7 @@ struct common_arg { std::set examples = {LLAMA_EXAMPLE_COMMON}; std::set excludes = {}; std::vector args; + std::vector args_neg; // for negated args like --no-xxx const char * value_hint = nullptr; // help text or example for arg value const char * value_hint_2 = nullptr; // for second arg value const char * env = nullptr; @@ -23,6 +26,9 @@ struct common_arg { void (*handler_string) (common_params & params, const std::string &) = nullptr; void (*handler_str_str)(common_params & params, const std::string &, const std::string &) = nullptr; void (*handler_int) (common_params & params, int) = nullptr; + void (*handler_bool) (common_params & params, bool) = nullptr; + + common_arg() = default; common_arg( const std::initializer_list & args, @@ -44,6 +50,13 @@ struct common_arg { void (*handler)(common_params & params) ) : args(args), help(help), handler_void(handler) {} + common_arg( + const std::initializer_list & args, + const std::initializer_list & args_neg, + const std::string & help, + void (*handler)(common_params & params, bool) + ) : args(args), args_neg(args_neg), help(help), handler_bool(handler) {} + // support 2 values for arg common_arg( const std::initializer_list & args, @@ -61,9 +74,33 @@ struct common_arg { bool is_exclude(enum llama_example ex); bool get_value_from_env(std::string & output) const; bool has_value_from_env() const; - std::string to_string(); + std::string to_string() const; + + // for using as key in std::map + bool operator<(const common_arg& other) const { + if (args.empty() || other.args.empty()) { + return false; + } + return strcmp(args[0], other.args[0]) < 0; + } + bool operator==(const common_arg& other) const { + if (args.empty() || other.args.empty()) { + return false; + } + return strcmp(args[0], other.args[0]) == 0; + } + + // get all args and env vars (including negated args/env) + std::vector get_args() const; + std::vector get_env() const; }; +namespace common_arg_utils { + bool is_truthy(const std::string & value); + bool is_falsey(const std::string & value); + bool is_autoy(const std::string & value); +} + struct common_params_context { enum llama_example ex = LLAMA_EXAMPLE_COMMON; common_params & params; @@ -76,7 +113,11 @@ struct common_params_context { // if one argument has invalid value, it will automatically display usage of the specific argument (and not the full usage message) bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr); -// function to be used by test-arg-parser +// parse input arguments from CLI into a map +// TODO: support repeated args in the future +bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map & out_map); + +// initialize argument parser context - used by test-arg-parser and preset common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr); struct common_remote_params { diff --git a/common/chat.cpp b/common/chat.cpp index d370b57703..b6ebb8db75 100644 --- a/common/chat.cpp +++ b/common/chat.cpp @@ -1,5 +1,6 @@ #include "chat.h" #include "chat-parser.h" +#include "chat-peg-parser.h" #include "common.h" #include "json-partial.h" #include "json-schema-to-grammar.h" @@ -150,6 +151,7 @@ struct templates_params { common_chat_tool_choice tool_choice; json json_schema; bool parallel_tool_calls; + common_reasoning_format reasoning_format; bool stream; std::string grammar; bool add_generation_prompt = true; @@ -589,6 +591,16 @@ common_chat_templates_ptr common_chat_templates_init( "{%- if false %}"); } + // TODO @aldehir : this is a temporary fix, pending Minja changes + // Ref: https://github.com/ggml-org/llama.cpp/pull/17713#issuecomment-3631342664 + if (default_template_src.find("[TOOL_CALLS]") != std::string::npos + // search for the error message and patch it + && default_template_src.find("if (message['content'] is none or") != std::string::npos) { + string_replace_all(default_template_src, + "{%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %}", + "{%- if false %}"); + } + std::string token_bos = bos_token_override; std::string token_eos = eos_token_override; bool add_bos = false; @@ -988,6 +1000,118 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat return data; } +static common_chat_params common_chat_params_init_ministral_3(const common_chat_template & tmpl, const struct templates_params & inputs) { + common_chat_params data; + + // Build up messages to follow the format: https://huggingface.co/mistralai/Ministral-3-14B-Reasoning-2512/blob/main/chat_template.jinja + auto adjusted_messages = json::array(); + for (const auto & msg : inputs.messages) { + auto role = msg.value("role", ""); + if (role != "system" && role != "assistant") { + // Only adjust system and assistant messages. Interestingly, the system message may contain thinking. + adjusted_messages.push_back(msg); + continue; + } + + auto content = json::array(); + + // If message contains `reasoning_content`, add it as a block of type `thinking` + if (msg.contains("reasoning_content") && msg.at("reasoning_content").is_string()) { + content.push_back({ + {"type", "thinking"}, + {"thinking", msg.at("reasoning_content").get()}, + }); + } + + // If message contains `content`, add it as a block of type `text` + if (msg.contains("content")) { + if (msg.at("content").is_string()) { + content.push_back({ + {"type", "text"}, + {"text", msg.at("content").get()}, + }); + } else if (msg.at("content").is_array()) { + auto blocks = msg.at("content"); + content.insert(content.end(), blocks.begin(), blocks.end()); + } + } + + auto adjusted = msg; + adjusted["content"] = content; + adjusted.erase("reasoning_content"); + adjusted_messages.push_back(adjusted); + } + + auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); + auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE; + auto include_grammar = true; + + data.prompt = apply(tmpl, inputs, /* messages_override = */ adjusted_messages); + data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; + data.preserved_tokens = { + "[THINK]", + "[/THINK]", + "[TOOL_CALLS]", + "[ARGS]", + }; + + auto parser = build_chat_peg_native_parser([&](common_chat_peg_native_builder & p) { + auto reasoning = extract_reasoning ? p.optional("[THINK]" + p.reasoning(p.until("[/THINK]")) + "[/THINK]") : p.eps(); + + // Response format parser + if (inputs.json_schema.is_object() && !inputs.json_schema.empty()) { + // Ministral wants to emit json surrounded by code fences + return reasoning << "```json" << p.content(p.schema(p.json(), "response-format", inputs.json_schema)) << "```"; + } + + // Tool call parser + if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) { + auto tool_choice = p.choice(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + std::string name = function.at("name"); + const auto & schema = function.at("parameters"); + + tool_choice |= p.rule("tool-" + name, + p.tool_open(p.tool_name(p.literal(name)) + "[ARGS]") + + p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema)) + ); + }); + + auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0; + auto max_calls = inputs.parallel_tool_calls ? -1 : 1; + auto tool_calls = p.trigger_rule("tool-call", p.repeat("[TOOL_CALLS]" + tool_choice, min_calls, max_calls)); + + return reasoning << p.content(p.until("[TOOL_CALLS]")) << tool_calls; + } + + // Content only parser + include_grammar = false; + return reasoning << p.content(p.rest()); + }); + + data.parser = parser.save(); + + if (include_grammar) { + data.grammar_lazy = has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO; + + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + auto schema = function.at("parameters"); + builder.resolve_refs(schema); + }); + parser.build_grammar(builder, data.grammar_lazy); + }); + + data.grammar_triggers = { + {COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "[TOOL_CALLS]"} + }; + } + + return data; +} + static common_chat_params common_chat_params_init_magistral(const common_chat_template & tmpl, const struct templates_params & inputs) { common_chat_params data; data.prompt = apply(tmpl, inputs); @@ -2390,6 +2514,7 @@ static common_chat_params common_chat_templates_apply_jinja( params.messages = common_chat_msgs_to_json_oaicompat(inputs.messages, /* concat_text= */ !tmpl.original_caps().requires_typed_content); params.add_generation_prompt = inputs.add_generation_prompt; params.tool_choice = inputs.tool_choice; + params.reasoning_format = inputs.reasoning_format; params.enable_thinking = inputs.enable_thinking; params.grammar = inputs.grammar; params.now = inputs.now; @@ -2568,6 +2693,13 @@ static common_chat_params common_chat_templates_apply_jinja( return common_chat_params_init_llama_3_x(tmpl, params, allow_python_tag_builtin_tools); } + // Ministral/Mistral Large 3 + if (src.find("[SYSTEM_PROMPT]") != std::string::npos && + src.find("[TOOL_CALLS]") != std::string::npos && + src.find("[ARGS]") != std::string::npos) { + return common_chat_params_init_ministral_3(tmpl, params); + } + if (src.find("[THINK]") != std::string::npos && src.find("[/THINK]") != std::string::npos) { return common_chat_params_init_magistral(tmpl, params); } diff --git a/common/common.cpp b/common/common.cpp index 0497f90a28..5a8cf52485 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1013,31 +1013,40 @@ bool tty_can_use_colors() { // Model utils // -static inline void common_init_sampler_from_model( +// TODO: move to common/sampling +static void common_init_sampler_from_model( const llama_model * model, common_params_sampling & sparams) { const uint64_t config = sparams.user_sampling_config; auto get_int32 = [&](const char * key, int32_t & dst, uint64_t user_config) { - if (config & user_config) return; + if (config & user_config) { + return; + } char buf[64] = {0}; if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) { char * end = nullptr; int32_t v = strtol(buf, &end, 10); - if (end && end != buf) dst = v; + if (end && end != buf) { + dst = v; + } } }; auto get_float = [&](const char * key, float & dst, uint64_t user_config) { - if (config & user_config) return; + if (config & user_config) { + return; + } char buf[128] = {0}; if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) { char * end = nullptr; float v = strtof(buf, &end); - if (end && end != buf) dst = v; + if (end && end != buf) { + dst = v; + } } }; @@ -1065,31 +1074,125 @@ static inline void common_init_sampler_from_model( get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA), sparams.mirostat_eta, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA); } -struct common_init_result common_init_from_params(common_params & params) { - common_init_result iparams; +struct common_init_result::impl { + impl() = default; + ~impl() = default; + + llama_model_ptr model; + llama_context_ptr context; + + std::vector lora; + + std::vector samplers; +}; + +common_init_result::common_init_result(common_params & params) : + pimpl(new impl{}) { auto mparams = common_model_params_to_llama(params); + auto cparams = common_context_params_to_llama(params); + + if (params.fit_params) { + LOG_INF("%s: fitting params to device memory, to report bugs during this step use -fit off (or --verbose if you can't)\n", __func__); + llama_params_fit(params.model.path.c_str(), &mparams, &cparams, + params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target, params.fit_params_min_ctx, + params.verbosity >= 4 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR); + } llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams); if (model == NULL) { - LOG_ERR("%s: failed to load model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n", - __func__, params.model.path.c_str()); - return iparams; + return; } - common_init_sampler_from_model(model, params.sampling); + pimpl->model.reset(model); const llama_vocab * vocab = llama_model_get_vocab(model); - auto cparams = common_context_params_to_llama(params); + // updates params.sampling + // TODO: fix naming + common_init_sampler_from_model(model, params.sampling); + + if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) { + LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__); + params.sampling.ignore_eos = false; + } + + // initialize once + for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) { + if (llama_vocab_is_eog(vocab, i)) { + LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(vocab, i).c_str(), -INFINITY); + params.sampling.logit_bias_eog.push_back({i, -INFINITY}); + } + } + + if (params.sampling.ignore_eos) { + // add EOG biases to the active set of logit biases + params.sampling.logit_bias.insert( + params.sampling.logit_bias.end(), + params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end()); + } + + //if (params.sampling.penalty_last_n == -1) { + // LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx)); + // params.sampling.penalty_last_n = llama_n_ctx(lctx); + //} + + //if (params.sampling.dry_penalty_last_n == -1) { + // LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx)); + // params.sampling.dry_penalty_last_n = llama_n_ctx(lctx); + //} + + pimpl->samplers.resize(cparams.n_seq_max); + + for (int i = 0; i < (int) cparams.n_seq_max; ++i) { + pimpl->samplers[i].reset(common_sampler_init(model, params.sampling)); + } llama_context * lctx = llama_init_from_model(model, cparams); if (lctx == NULL) { - LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n", - __func__, params.model.path.c_str()); - llama_model_free(model); - return iparams; + LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str()); + return; } + pimpl->context.reset(lctx); +} + +llama_model * common_init_result::model() { + return pimpl->model.get(); +} + +llama_context * common_init_result::context() { + return pimpl->context.get(); +} + +common_sampler * common_init_result::sampler(llama_seq_id seq_id) { + return pimpl->samplers[seq_id].get(); +} + +std::vector & common_init_result::lora() { + return pimpl->lora; +} + +void common_init_result::free_context() { + pimpl->context.reset(); +} + +common_init_result_ptr common_init_from_params(common_params & params) { + common_init_result_ptr res(new common_init_result(params)); + + llama_model * model = res->model(); + if (model == NULL) { + LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.path.c_str()); + return res; + } + + llama_context * lctx = res->context(); + if (lctx == NULL) { + LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str()); + return res; + } + + const llama_vocab * vocab = llama_model_get_vocab(model); + if (params.ctx_shift && !llama_memory_can_shift(llama_get_memory(lctx))) { LOG_WRN("%s: KV cache shifting is not supported for this context, disabling KV cache shifting\n", __func__); params.ctx_shift = false; @@ -1101,10 +1204,7 @@ struct common_init_result common_init_from_params(common_params & params) { const auto cvec = common_control_vector_load(params.control_vectors); if (cvec.n_embd == -1) { - llama_free(lctx); - llama_model_free(model); - - return iparams; + return res; } int err = llama_apply_adapter_cvec( @@ -1115,10 +1215,7 @@ struct common_init_result common_init_from_params(common_params & params) { params.control_vector_layer_start, params.control_vector_layer_end); if (err) { - llama_free(lctx); - llama_model_free(model); - - return iparams; + return res; } } @@ -1142,10 +1239,7 @@ struct common_init_result common_init_from_params(common_params & params) { } if (!ok) { - llama_free(lctx); - llama_model_free(model); - - return iparams; + return res; } } @@ -1155,9 +1249,7 @@ struct common_init_result common_init_from_params(common_params & params) { lora.reset(llama_adapter_lora_init(model, la.path.c_str())); if (lora == nullptr) { LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str()); - llama_free(lctx); - llama_model_free(model); - return iparams; + return res; } char buf[1024]; @@ -1166,43 +1258,13 @@ struct common_init_result common_init_from_params(common_params & params) { la.task_name = buf; llama_adapter_meta_val_str(la.ptr, "adapter.lora.prompt_prefix", buf, sizeof(buf)); la.prompt_prefix = buf; - iparams.lora.emplace_back(std::move(lora)); // copy to list of loaded adapters + res->lora().emplace_back(std::move(lora)); // copy to list of loaded adapters } if (!params.lora_init_without_apply) { common_set_adapter_lora(lctx, params.lora_adapters); } - if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) { - LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__); - params.sampling.ignore_eos = false; - } - - // initialize once - for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) { - if (llama_vocab_is_eog(vocab, i)) { - LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY); - params.sampling.logit_bias_eog.push_back({i, -INFINITY}); - } - } - - if (params.sampling.ignore_eos) { - // add EOG biases to the active set of logit biases - params.sampling.logit_bias.insert( - params.sampling.logit_bias.end(), - params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end()); - } - - if (params.sampling.penalty_last_n == -1) { - LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx)); - params.sampling.penalty_last_n = llama_n_ctx(lctx); - } - - if (params.sampling.dry_penalty_last_n == -1) { - LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx)); - params.sampling.dry_penalty_last_n = llama_n_ctx(lctx); - } - if (params.warmup) { LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__); @@ -1241,12 +1303,11 @@ struct common_init_result common_init_from_params(common_params & params) { llama_set_warmup(lctx, false); } - iparams.model.reset(model); - iparams.context.reset(lctx); - - return iparams; + return res; } +common_init_result::~common_init_result() = default; + std::string get_model_endpoint() { const char * model_endpoint_env = getenv("MODEL_ENDPOINT"); // We still respect the use of environment-variable "HF_ENDPOINT" for backward-compatibility. @@ -1255,7 +1316,9 @@ std::string get_model_endpoint() { std::string model_endpoint = "https://huggingface.co/"; if (endpoint_env) { model_endpoint = endpoint_env; - if (model_endpoint.back() != '/') model_endpoint += '/'; + if (model_endpoint.back() != '/') { + model_endpoint += '/'; + } } return model_endpoint; } diff --git a/common/common.h b/common/common.h index d28e48991c..d70744840f 100644 --- a/common/common.h +++ b/common/common.h @@ -82,7 +82,8 @@ int32_t cpu_get_num_math(); enum llama_example { LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_SPECULATIVE, - LLAMA_EXAMPLE_MAIN, + LLAMA_EXAMPLE_COMPLETION, + LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_RETRIEVAL, @@ -98,6 +99,7 @@ enum llama_example { LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_DIFFUSION, LLAMA_EXAMPLE_FINETUNE, + LLAMA_EXAMPLE_FIT_PARAMS, LLAMA_EXAMPLE_COUNT, }; @@ -194,7 +196,6 @@ struct common_params_sampling { std::vector dry_sequence_breakers = {"\n", ":", "\"", "*"}; // default sequence breakers for DRY - std::vector samplers = { COMMON_SAMPLER_TYPE_PENALTIES, COMMON_SAMPLER_TYPE_DRY, @@ -215,6 +216,10 @@ struct common_params_sampling { std::vector logit_bias; // logit biases to apply std::vector logit_bias_eog; // pre-calculated logit biases for EOG tokens + bool has_logit_bias() const { + return !logit_bias.empty(); + } + // print the parameters into a string std::string print() const; }; @@ -302,8 +307,8 @@ struct lr_opt { struct ggml_opt_optimizer_params common_opt_lr_pars(void * userdata); struct common_params { - int32_t n_predict = -1; // new tokens to predict - int32_t n_ctx = 4096; // context size + int32_t n_predict = -1; // max. number of new tokens to predict, -1 == no limit + int32_t n_ctx = 0; // context size, 0 == context the model was trained with int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS) int32_t n_ubatch = 512; // physical batch size for prompt processing (must be >=32 to use BLAS) int32_t n_keep = 0; // number of tokens to keep from initial prompt @@ -324,9 +329,12 @@ struct common_params { // offload params std::vector devices; // devices to use for offloading - int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) - int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors - float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs + int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) + int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors + float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs + bool fit_params = true; // whether to fit unset model/context parameters to free device memory + size_t fit_params_target = 1024 * 1024*1024; // margin per device in bytes for fitting parameters to free memory + int32_t fit_params_min_ctx = 4096; // minimum context size to set when trying to reduce memory use enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs @@ -406,6 +414,7 @@ struct common_params { bool simple_io = false; // improves compatibility with subprocesses and limited consoles bool cont_batching = true; // insert new sequences for decoding on-the-fly bool no_perf = false; // disable performance metrics + bool show_timings = true; // show timing information on CLI bool ctx_shift = false; // context shift on infinite text generation bool swa_full = false; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055) bool kv_unified = false; // enable unified KV cache @@ -462,7 +471,7 @@ struct common_params { std::string public_path = ""; // NOLINT std::string api_prefix = ""; // NOLINT std::string chat_template = ""; // NOLINT - bool use_jinja = false; // NOLINT + bool use_jinja = true; // NOLINT bool enable_chat_template = true; common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; int reasoning_budget = -1; @@ -482,9 +491,10 @@ struct common_params { bool endpoint_metrics = false; // router server configs - std::string models_dir = ""; // directory containing models for the router server - int models_max = 4; // maximum number of models to load simultaneously - bool models_autoload = true; // automatically load models when requested via the router server + std::string models_dir = ""; // directory containing models for the router server + std::string models_preset = ""; // directory containing model presets for the router server + int models_max = 4; // maximum number of models to load simultaneously + bool models_autoload = true; // automatically load models when requested via the router server bool log_json = false; @@ -666,15 +676,29 @@ bool tty_can_use_colors(); // Model utils // -// note: defines object's lifetime -struct common_init_result { - llama_model_ptr model; - llama_context_ptr context; +struct common_sampler; - std::vector lora; +// note: defines the model, context, samplers, ets. lifetimes +struct common_init_result { + common_init_result(common_params & params); + ~common_init_result(); + + llama_model * model(); + llama_context * context(); + common_sampler * sampler(llama_seq_id seq_id); + + std::vector & lora(); + + void free_context(); + +private: + struct impl; + std::unique_ptr pimpl; }; -struct common_init_result common_init_from_params(common_params & params); +using common_init_result_ptr = std::unique_ptr; + +common_init_result_ptr common_init_from_params(common_params & params); struct llama_model_params common_model_params_to_llama ( common_params & params); struct llama_context_params common_context_params_to_llama(const common_params & params); diff --git a/common/console.cpp b/common/console.cpp index 078a8d678d..2ea178f81e 100644 --- a/common/console.cpp +++ b/common/console.cpp @@ -1,6 +1,16 @@ #include "console.h" +#include "log.h" #include #include +#include +#include +#include +#include +#include +#include +#include +#include +#include #if defined(_WIN32) #define WIN32_LEAN_AND_MEAN @@ -30,26 +40,44 @@ #define ANSI_COLOR_BLUE "\x1b[34m" #define ANSI_COLOR_MAGENTA "\x1b[35m" #define ANSI_COLOR_CYAN "\x1b[36m" +#define ANSI_COLOR_GRAY "\x1b[90m" #define ANSI_COLOR_RESET "\x1b[0m" #define ANSI_BOLD "\x1b[1m" namespace console { +#if defined (_WIN32) + namespace { + // Use private-use unicode values to represent special keys that are not reported + // as characters (e.g. arrows on Windows). These values should never clash with + // real input and let the rest of the code handle navigation uniformly. + static constexpr char32_t KEY_ARROW_LEFT = 0xE000; + static constexpr char32_t KEY_ARROW_RIGHT = 0xE001; + static constexpr char32_t KEY_ARROW_UP = 0xE002; + static constexpr char32_t KEY_ARROW_DOWN = 0xE003; + static constexpr char32_t KEY_HOME = 0xE004; + static constexpr char32_t KEY_END = 0xE005; + static constexpr char32_t KEY_CTRL_ARROW_LEFT = 0xE006; + static constexpr char32_t KEY_CTRL_ARROW_RIGHT = 0xE007; + static constexpr char32_t KEY_DELETE = 0xE008; + } + // // Console state // +#endif - static bool advanced_display = false; - static bool simple_io = true; - static display_t current_display = reset; + static bool advanced_display = false; + static bool simple_io = true; + static display_type current_display = DISPLAY_TYPE_RESET; - static FILE* out = stdout; + static FILE* out = stdout; #if defined (_WIN32) - static void* hConsole; + static void* hConsole; #else - static FILE* tty = nullptr; - static termios initial_state; + static FILE* tty = nullptr; + static termios initial_state; #endif // @@ -120,7 +148,7 @@ namespace console { void cleanup() { // Reset console display - set_display(reset); + set_display(DISPLAY_TYPE_RESET); #if !defined(_WIN32) // Restore settings on POSIX systems @@ -140,20 +168,26 @@ namespace console { // // Keep track of current display and only emit ANSI code if it changes - void set_display(display_t display) { + void set_display(display_type display) { if (advanced_display && current_display != display) { - fflush(stdout); + common_log_flush(common_log_main()); switch(display) { - case reset: + case DISPLAY_TYPE_RESET: fprintf(out, ANSI_COLOR_RESET); break; - case prompt: + case DISPLAY_TYPE_INFO: + fprintf(out, ANSI_COLOR_MAGENTA); + break; + case DISPLAY_TYPE_PROMPT: fprintf(out, ANSI_COLOR_YELLOW); break; - case user_input: + case DISPLAY_TYPE_REASONING: + fprintf(out, ANSI_COLOR_GRAY); + break; + case DISPLAY_TYPE_USER_INPUT: fprintf(out, ANSI_BOLD ANSI_COLOR_GREEN); break; - case error: + case DISPLAY_TYPE_ERROR: fprintf(out, ANSI_BOLD ANSI_COLOR_RED); } current_display = display; @@ -176,7 +210,18 @@ namespace console { if (record.EventType == KEY_EVENT && record.Event.KeyEvent.bKeyDown) { wchar_t wc = record.Event.KeyEvent.uChar.UnicodeChar; if (wc == 0) { - continue; + const DWORD ctrl_mask = LEFT_CTRL_PRESSED | RIGHT_CTRL_PRESSED; + const bool ctrl_pressed = (record.Event.KeyEvent.dwControlKeyState & ctrl_mask) != 0; + switch (record.Event.KeyEvent.wVirtualKeyCode) { + case VK_LEFT: return ctrl_pressed ? KEY_CTRL_ARROW_LEFT : KEY_ARROW_LEFT; + case VK_RIGHT: return ctrl_pressed ? KEY_CTRL_ARROW_RIGHT : KEY_ARROW_RIGHT; + case VK_UP: return KEY_ARROW_UP; + case VK_DOWN: return KEY_ARROW_DOWN; + case VK_HOME: return KEY_HOME; + case VK_END: return KEY_END; + case VK_DELETE: return KEY_DELETE; + default: continue; + } } if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate @@ -315,6 +360,52 @@ namespace console { #endif } + static char32_t decode_utf8(const std::string & input, size_t pos, size_t & advance) { + unsigned char c = static_cast(input[pos]); + if ((c & 0x80u) == 0u) { + advance = 1; + return c; + } + if ((c & 0xE0u) == 0xC0u && pos + 1 < input.size()) { + unsigned char c1 = static_cast(input[pos + 1]); + if ((c1 & 0xC0u) != 0x80u) { + advance = 1; + return 0xFFFD; + } + advance = 2; + return ((c & 0x1Fu) << 6) | (static_cast(input[pos + 1]) & 0x3Fu); + } + if ((c & 0xF0u) == 0xE0u && pos + 2 < input.size()) { + unsigned char c1 = static_cast(input[pos + 1]); + unsigned char c2 = static_cast(input[pos + 2]); + if ((c1 & 0xC0u) != 0x80u || (c2 & 0xC0u) != 0x80u) { + advance = 1; + return 0xFFFD; + } + advance = 3; + return ((c & 0x0Fu) << 12) | + ((static_cast(input[pos + 1]) & 0x3Fu) << 6) | + (static_cast(input[pos + 2]) & 0x3Fu); + } + if ((c & 0xF8u) == 0xF0u && pos + 3 < input.size()) { + unsigned char c1 = static_cast(input[pos + 1]); + unsigned char c2 = static_cast(input[pos + 2]); + unsigned char c3 = static_cast(input[pos + 3]); + if ((c1 & 0xC0u) != 0x80u || (c2 & 0xC0u) != 0x80u || (c3 & 0xC0u) != 0x80u) { + advance = 1; + return 0xFFFD; + } + advance = 4; + return ((c & 0x07u) << 18) | + ((static_cast(input[pos + 1]) & 0x3Fu) << 12) | + ((static_cast(input[pos + 2]) & 0x3Fu) << 6) | + (static_cast(input[pos + 3]) & 0x3Fu); + } + + advance = 1; + return 0xFFFD; // replacement character for invalid input + } + static void append_utf8(char32_t ch, std::string & out) { if (ch <= 0x7F) { out.push_back(static_cast(ch)); @@ -336,22 +427,319 @@ namespace console { } // Helper function to remove the last UTF-8 character from a string - static void pop_back_utf8_char(std::string & line) { - if (line.empty()) { + static size_t prev_utf8_char_pos(const std::string & line, size_t pos) { + if (pos == 0) return 0; + pos--; + while (pos > 0 && (line[pos] & 0xC0) == 0x80) { + pos--; + } + return pos; + } + + static size_t next_utf8_char_pos(const std::string & line, size_t pos) { + if (pos >= line.length()) return line.length(); + pos++; + while (pos < line.length() && (line[pos] & 0xC0) == 0x80) { + pos++; + } + return pos; + } + + static void move_cursor(int delta); + static void move_word_left(size_t & char_pos, size_t & byte_pos, const std::vector & widths, const std::string & line); + static void move_word_right(size_t & char_pos, size_t & byte_pos, const std::vector & widths, const std::string & line); + static void move_to_line_start(size_t & char_pos, size_t & byte_pos, const std::vector & widths); + static void move_to_line_end(size_t & char_pos, size_t & byte_pos, const std::vector & widths, const std::string & line); + + static void delete_at_cursor(std::string & line, std::vector & widths, size_t & char_pos, size_t & byte_pos) { + if (char_pos >= widths.size()) { return; } - size_t pos = line.length() - 1; + size_t next_pos = next_utf8_char_pos(line, byte_pos); + int w = widths[char_pos]; + size_t char_len = next_pos - byte_pos; - // Find the start of the last UTF-8 character (checking up to 4 bytes back) - for (size_t i = 0; i < 3 && pos > 0; ++i, --pos) { - if ((line[pos] & 0xC0) != 0x80) { - break; // Found the start of the character - } + line.erase(byte_pos, char_len); + widths.erase(widths.begin() + char_pos); + + size_t p = byte_pos; + int tail_width = 0; + for (size_t i = char_pos; i < widths.size(); ++i) { + size_t following = next_utf8_char_pos(line, p); + put_codepoint(line.c_str() + p, following - p, widths[i]); + tail_width += widths[i]; + p = following; } - line.erase(pos); + + for (int i = 0; i < w; ++i) { + fputc(' ', out); + } + + move_cursor(-(tail_width + w)); } + static void clear_current_line(const std::vector & widths) { + int total_width = 0; + for (int w : widths) { + total_width += (w > 0 ? w : 1); + } + + if (total_width > 0) { + std::string spaces(total_width, ' '); + fwrite(spaces.c_str(), 1, total_width, out); + move_cursor(-total_width); + } + } + + static void set_line_contents(std::string new_line, std::string & line, std::vector & widths, size_t & char_pos, + size_t & byte_pos) { + move_to_line_start(char_pos, byte_pos, widths); + clear_current_line(widths); + + line = std::move(new_line); + widths.clear(); + byte_pos = 0; + char_pos = 0; + + size_t idx = 0; + while (idx < line.size()) { + size_t advance = 0; + char32_t cp = decode_utf8(line, idx, advance); + int expected_width = estimateWidth(cp); + int real_width = put_codepoint(line.c_str() + idx, advance, expected_width); + if (real_width < 0) real_width = 0; + widths.push_back(real_width); + idx += advance; + ++char_pos; + byte_pos = idx; + } + } + + static void move_to_line_start(size_t & char_pos, size_t & byte_pos, const std::vector & widths) { + int back_width = 0; + for (size_t i = 0; i < char_pos; ++i) { + back_width += widths[i]; + } + move_cursor(-back_width); + char_pos = 0; + byte_pos = 0; + } + + static void move_to_line_end(size_t & char_pos, size_t & byte_pos, const std::vector & widths, const std::string & line) { + int forward_width = 0; + for (size_t i = char_pos; i < widths.size(); ++i) { + forward_width += widths[i]; + } + move_cursor(forward_width); + char_pos = widths.size(); + byte_pos = line.length(); + } + + static bool has_ctrl_modifier(const std::string & params) { + size_t start = 0; + while (start < params.size()) { + size_t end = params.find(';', start); + size_t len = (end == std::string::npos) ? params.size() - start : end - start; + if (len > 0) { + int value = 0; + for (size_t i = 0; i < len; ++i) { + char ch = params[start + i]; + if (!std::isdigit(static_cast(ch))) { + value = -1; + break; + } + value = value * 10 + (ch - '0'); + } + if (value == 5) { + return true; + } + } + + if (end == std::string::npos) { + break; + } + start = end + 1; + } + return false; + } + + static bool is_space_codepoint(char32_t cp) { + return std::iswspace(static_cast(cp)) != 0; + } + + static void move_word_left(size_t & char_pos, size_t & byte_pos, const std::vector & widths, const std::string & line) { + if (char_pos == 0) { + return; + } + + size_t new_char_pos = char_pos; + size_t new_byte_pos = byte_pos; + int move_width = 0; + + while (new_char_pos > 0) { + size_t prev_byte = prev_utf8_char_pos(line, new_byte_pos); + size_t advance = 0; + char32_t cp = decode_utf8(line, prev_byte, advance); + if (!is_space_codepoint(cp)) { + break; + } + move_width += widths[new_char_pos - 1]; + new_char_pos--; + new_byte_pos = prev_byte; + } + + while (new_char_pos > 0) { + size_t prev_byte = prev_utf8_char_pos(line, new_byte_pos); + size_t advance = 0; + char32_t cp = decode_utf8(line, prev_byte, advance); + if (is_space_codepoint(cp)) { + break; + } + move_width += widths[new_char_pos - 1]; + new_char_pos--; + new_byte_pos = prev_byte; + } + + move_cursor(-move_width); + char_pos = new_char_pos; + byte_pos = new_byte_pos; + } + + static void move_word_right(size_t & char_pos, size_t & byte_pos, const std::vector & widths, const std::string & line) { + if (char_pos >= widths.size()) { + return; + } + + size_t new_char_pos = char_pos; + size_t new_byte_pos = byte_pos; + int move_width = 0; + + while (new_char_pos < widths.size()) { + size_t advance = 0; + char32_t cp = decode_utf8(line, new_byte_pos, advance); + if (!is_space_codepoint(cp)) { + break; + } + move_width += widths[new_char_pos]; + new_char_pos++; + new_byte_pos += advance; + } + + while (new_char_pos < widths.size()) { + size_t advance = 0; + char32_t cp = decode_utf8(line, new_byte_pos, advance); + if (is_space_codepoint(cp)) { + break; + } + move_width += widths[new_char_pos]; + new_char_pos++; + new_byte_pos += advance; + } + + while (new_char_pos < widths.size()) { + size_t advance = 0; + char32_t cp = decode_utf8(line, new_byte_pos, advance); + if (!is_space_codepoint(cp)) { + break; + } + move_width += widths[new_char_pos]; + new_char_pos++; + new_byte_pos += advance; + } + + move_cursor(move_width); + char_pos = new_char_pos; + byte_pos = new_byte_pos; + } + + static void move_cursor(int delta) { + if (delta == 0) return; +#if defined(_WIN32) + if (hConsole != NULL) { + CONSOLE_SCREEN_BUFFER_INFO bufferInfo; + GetConsoleScreenBufferInfo(hConsole, &bufferInfo); + COORD newCursorPosition = bufferInfo.dwCursorPosition; + int width = bufferInfo.dwSize.X; + int newX = newCursorPosition.X + delta; + int newY = newCursorPosition.Y; + + while (newX >= width) { + newX -= width; + newY++; + } + while (newX < 0) { + newX += width; + newY--; + } + + newCursorPosition.X = newX; + newCursorPosition.Y = newY; + SetConsoleCursorPosition(hConsole, newCursorPosition); + } +#else + if (delta < 0) { + for (int i = 0; i < -delta; i++) fprintf(out, "\b"); + } else { + for (int i = 0; i < delta; i++) fprintf(out, "\033[C"); + } +#endif + } + + struct history_t { + std::vector entries; + size_t viewing_idx = SIZE_MAX; + std::string backup_line; // current line before viewing history + void add(const std::string & line) { + if (line.empty()) { + return; + } + // avoid duplicates with the last entry + if (entries.empty() || entries.back() != line) { + entries.push_back(line); + } + // also clear viewing state + end_viewing(); + } + bool prev(std::string & cur_line) { + if (entries.empty()) { + return false; + } + if (viewing_idx == SIZE_MAX) { + return false; + } + if (viewing_idx > 0) { + viewing_idx--; + } + cur_line = entries[viewing_idx]; + return true; + } + bool next(std::string & cur_line) { + if (entries.empty() || viewing_idx == SIZE_MAX) { + return false; + } + viewing_idx++; + if (viewing_idx >= entries.size()) { + cur_line = backup_line; + end_viewing(); + } else { + cur_line = entries[viewing_idx]; + } + return true; + } + void begin_viewing(const std::string & line) { + backup_line = line; + viewing_idx = entries.size(); + } + void end_viewing() { + viewing_idx = SIZE_MAX; + backup_line.clear(); + } + bool is_viewing() const { + return viewing_idx != SIZE_MAX; + } + } history; + static bool readline_advanced(std::string & line, bool multiline_input) { if (out != stdout) { fflush(stdout); @@ -362,8 +750,33 @@ namespace console { bool is_special_char = false; bool end_of_stream = false; + size_t byte_pos = 0; // current byte index + size_t char_pos = 0; // current character index (one char can be multiple bytes) + char32_t input_char; while (true) { + assert(char_pos <= byte_pos); + assert(char_pos <= widths.size()); + auto history_prev = [&]() { + if (!history.is_viewing()) { + history.begin_viewing(line); + } + std::string new_line; + if (!history.prev(new_line)) { + return; + } + set_line_contents(new_line, line, widths, char_pos, byte_pos); + }; + auto history_next = [&]() { + if (history.is_viewing()) { + std::string new_line; + if (!history.next(new_line)) { + return; + } + set_line_contents(new_line, line, widths, char_pos, byte_pos); + } + }; + fflush(out); // Ensure all output is displayed before waiting for input input_char = getchar32(); @@ -371,20 +784,83 @@ namespace console { break; } - if (input_char == (char32_t) WEOF || input_char == 0x04 /* Ctrl+D*/) { + if (input_char == (char32_t) WEOF || input_char == 0x04 /* Ctrl+D */) { end_of_stream = true; break; } if (is_special_char) { - set_display(user_input); replace_last(line.back()); is_special_char = false; } if (input_char == '\033') { // Escape sequence char32_t code = getchar32(); - if (code == '[' || code == 0x1B) { + if (code == '[') { + std::string params; + while (true) { + code = getchar32(); + if ((code >= 'A' && code <= 'Z') || (code >= 'a' && code <= 'z') || code == '~' || code == (char32_t) WEOF) { + break; + } + params.push_back(static_cast(code)); + } + + const bool ctrl_modifier = has_ctrl_modifier(params); + + if (code == 'D') { // left + if (ctrl_modifier) { + move_word_left(char_pos, byte_pos, widths, line); + } else if (char_pos > 0) { + int w = widths[char_pos - 1]; + move_cursor(-w); + char_pos--; + byte_pos = prev_utf8_char_pos(line, byte_pos); + } + } else if (code == 'C') { // right + if (ctrl_modifier) { + move_word_right(char_pos, byte_pos, widths, line); + } else if (char_pos < widths.size()) { + int w = widths[char_pos]; + move_cursor(w); + char_pos++; + byte_pos = next_utf8_char_pos(line, byte_pos); + } + } else if (code == 'H') { // home + move_to_line_start(char_pos, byte_pos, widths); + } else if (code == 'F') { // end + move_to_line_end(char_pos, byte_pos, widths, line); + } else if (code == 'A' || code == 'B') { + // up/down + if (code == 'A') { + history_prev(); + is_special_char = false; + } else if (code == 'B') { + history_next(); + is_special_char = false; + } + } else if ((code == '~' || (code >= 'A' && code <= 'Z') || (code >= 'a' && code <= 'z')) && !params.empty()) { + std::string digits; + for (char ch : params) { + if (ch == ';') { + break; + } + if (std::isdigit(static_cast(ch))) { + digits.push_back(ch); + } + } + + if (code == '~') { + if (digits == "1" || digits == "7") { // home + move_to_line_start(char_pos, byte_pos, widths); + } else if (digits == "4" || digits == "8") { // end + move_to_line_end(char_pos, byte_pos, widths, line); + } else if (digits == "3") { // delete + delete_at_cursor(line, widths, char_pos, byte_pos); + } + } + } + } else if (code == 0x1B) { // Discard the rest of the escape sequence while ((code = getchar32()) != (char32_t) WEOF) { if ((code >= 'A' && code <= 'Z') || (code >= 'a' && code <= 'z') || code == '~') { @@ -392,32 +868,110 @@ namespace console { } } } +#if defined(_WIN32) + } else if (input_char == KEY_ARROW_LEFT) { + if (char_pos > 0) { + int w = widths[char_pos - 1]; + move_cursor(-w); + char_pos--; + byte_pos = prev_utf8_char_pos(line, byte_pos); + } + } else if (input_char == KEY_ARROW_RIGHT) { + if (char_pos < widths.size()) { + int w = widths[char_pos]; + move_cursor(w); + char_pos++; + byte_pos = next_utf8_char_pos(line, byte_pos); + } + } else if (input_char == KEY_CTRL_ARROW_LEFT) { + move_word_left(char_pos, byte_pos, widths, line); + } else if (input_char == KEY_CTRL_ARROW_RIGHT) { + move_word_right(char_pos, byte_pos, widths, line); + } else if (input_char == KEY_HOME) { + move_to_line_start(char_pos, byte_pos, widths); + } else if (input_char == KEY_END) { + move_to_line_end(char_pos, byte_pos, widths, line); + } else if (input_char == KEY_DELETE) { + delete_at_cursor(line, widths, char_pos, byte_pos); + } else if (input_char == KEY_ARROW_UP || input_char == KEY_ARROW_DOWN) { + if (input_char == KEY_ARROW_UP) { + history_prev(); + is_special_char = false; + } else if (input_char == KEY_ARROW_DOWN) { + history_next(); + is_special_char = false; + } +#endif } else if (input_char == 0x08 || input_char == 0x7F) { // Backspace - if (!widths.empty()) { - int count; - do { - count = widths.back(); - widths.pop_back(); - // Move cursor back, print space, and move cursor back again - for (int i = 0; i < count; i++) { - replace_last(' '); - pop_cursor(); - } - pop_back_utf8_char(line); - } while (count == 0 && !widths.empty()); + if (char_pos > 0) { + int w = widths[char_pos - 1]; + move_cursor(-w); + char_pos--; + size_t prev_pos = prev_utf8_char_pos(line, byte_pos); + size_t char_len = byte_pos - prev_pos; + byte_pos = prev_pos; + + // remove the character + line.erase(byte_pos, char_len); + widths.erase(widths.begin() + char_pos); + + // redraw tail + size_t p = byte_pos; + int tail_width = 0; + for (size_t i = char_pos; i < widths.size(); ++i) { + size_t next_p = next_utf8_char_pos(line, p); + put_codepoint(line.c_str() + p, next_p - p, widths[i]); + tail_width += widths[i]; + p = next_p; + } + + // clear display + for (int i = 0; i < w; ++i) { + fputc(' ', out); + } + move_cursor(-(tail_width + w)); } } else { - int offset = line.length(); - append_utf8(input_char, line); - int width = put_codepoint(line.c_str() + offset, line.length() - offset, estimateWidth(input_char)); - if (width < 0) { - width = 0; + // insert character + std::string new_char_str; + append_utf8(input_char, new_char_str); + int w = estimateWidth(input_char); + + if (char_pos == widths.size()) { + // insert at the end + line += new_char_str; + int real_w = put_codepoint(new_char_str.c_str(), new_char_str.length(), w); + if (real_w < 0) real_w = 0; + widths.push_back(real_w); + byte_pos += new_char_str.length(); + char_pos++; + } else { + // insert in middle + line.insert(byte_pos, new_char_str); + + int real_w = put_codepoint(new_char_str.c_str(), new_char_str.length(), w); + if (real_w < 0) real_w = 0; + + widths.insert(widths.begin() + char_pos, real_w); + + // print the tail + size_t p = byte_pos + new_char_str.length(); + int tail_width = 0; + for (size_t i = char_pos + 1; i < widths.size(); ++i) { + size_t next_p = next_utf8_char_pos(line, p); + put_codepoint(line.c_str() + p, next_p - p, widths[i]); + tail_width += widths[i]; + p = next_p; + } + + move_cursor(-tail_width); + + byte_pos += new_char_str.length(); + char_pos++; } - widths.push_back(width); } if (!line.empty() && (line.back() == '\\' || line.back() == '/')) { - set_display(prompt); replace_last(line.back()); is_special_char = true; } @@ -451,6 +1005,15 @@ namespace console { } } + if (!end_of_stream && !line.empty()) { + // remove the trailing newline for history storage + if (!line.empty() && line.back() == '\n') { + line.pop_back(); + } + // TODO: maybe support multiline history entries? + history.add(line); + } + fflush(out); return has_more; } @@ -493,12 +1056,82 @@ namespace console { } bool readline(std::string & line, bool multiline_input) { - set_display(user_input); - if (simple_io) { return readline_simple(line, multiline_input); } return readline_advanced(line, multiline_input); } + namespace spinner { + static const char LOADING_CHARS[] = {'|', '/', '-', '\\'}; + static std::condition_variable cv_stop; + static std::thread th; + static size_t frame = 0; // only modified by one thread + static bool running = false; + static std::mutex mtx; + static auto wait_time = std::chrono::milliseconds(100); + static void draw_next_frame() { + // don't need lock because only one thread modifies running + frame = (frame + 1) % sizeof(LOADING_CHARS); + replace_last(LOADING_CHARS[frame]); + fflush(out); + } + void start() { + std::unique_lock lock(mtx); + if (simple_io || running) { + return; + } + common_log_flush(common_log_main()); + fprintf(out, "%c", LOADING_CHARS[0]); + fflush(out); + frame = 1; + running = true; + th = std::thread([]() { + std::unique_lock lock(mtx); + while (true) { + if (cv_stop.wait_for(lock, wait_time, []{ return !running; })) { + break; + } + draw_next_frame(); + } + }); + } + void stop() { + { + std::unique_lock lock(mtx); + if (simple_io || !running) { + return; + } + running = false; + cv_stop.notify_all(); + } + if (th.joinable()) { + th.join(); + } + replace_last(' '); + pop_cursor(); + fflush(out); + } + } + + void log(const char * fmt, ...) { + va_list args; + va_start(args, fmt); + vfprintf(out, fmt, args); + va_end(args); + } + + void error(const char * fmt, ...) { + va_list args; + va_start(args, fmt); + display_type cur = current_display; + set_display(DISPLAY_TYPE_ERROR); + vfprintf(out, fmt, args); + set_display(cur); // restore previous color + va_end(args); + } + + void flush() { + fflush(out); + } } diff --git a/common/console.h b/common/console.h index ec175269b9..fad6d39531 100644 --- a/common/console.h +++ b/common/console.h @@ -2,18 +2,40 @@ #pragma once +#include "common.h" + #include -namespace console { - enum display_t { - reset = 0, - prompt, - user_input, - error - }; +enum display_type { + DISPLAY_TYPE_RESET = 0, + DISPLAY_TYPE_INFO, + DISPLAY_TYPE_PROMPT, + DISPLAY_TYPE_REASONING, + DISPLAY_TYPE_USER_INPUT, + DISPLAY_TYPE_ERROR +}; +namespace console { void init(bool use_simple_io, bool use_advanced_display); void cleanup(); - void set_display(display_t display); + void set_display(display_type display); bool readline(std::string & line, bool multiline_input); + + namespace spinner { + void start(); + void stop(); + } + + // note: the logging API below output directly to stdout + // it can negatively impact performance if used on inference thread + // only use in in a dedicated CLI thread + // for logging in inference thread, use log.h instead + + LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2) + void log(const char * fmt, ...); + + LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2) + void error(const char * fmt, ...); + + void flush(); } diff --git a/common/download.cpp b/common/download.cpp index ab68c53b43..ef87472560 100644 --- a/common/download.cpp +++ b/common/download.cpp @@ -12,6 +12,8 @@ #include #include #include +#include +#include #include #include #include @@ -472,36 +474,79 @@ std::pair> common_remote_get_content(const std::string & #elif defined(LLAMA_USE_HTTPLIB) -static bool is_output_a_tty() { +class ProgressBar { + static inline std::mutex mutex; + static inline std::map lines; + static inline int max_line = 0; + + static void cleanup(const ProgressBar * line) { + lines.erase(line); + if (lines.empty()) { + max_line = 0; + } + } + + static bool is_output_a_tty() { #if defined(_WIN32) - return _isatty(_fileno(stdout)); + return _isatty(_fileno(stdout)); #else - return isatty(1); + return isatty(1); #endif -} - -static void print_progress(size_t current, size_t total) { - if (!is_output_a_tty()) { - return; } - if (!total) { - return; +public: + ProgressBar() = default; + + ~ProgressBar() { + std::lock_guard lock(mutex); + cleanup(this); } - size_t width = 50; - size_t pct = (100 * current) / total; - size_t pos = (width * current) / total; + void update(size_t current, size_t total) { + if (!is_output_a_tty()) { + return; + } - std::cout << "[" - << std::string(pos, '=') - << (pos < width ? ">" : "") - << std::string(width - pos, ' ') - << "] " << std::setw(3) << pct << "% (" - << current / (1024 * 1024) << " MB / " - << total / (1024 * 1024) << " MB)\r"; - std::cout.flush(); -} + if (!total) { + return; + } + + std::lock_guard lock(mutex); + + if (lines.find(this) == lines.end()) { + lines[this] = max_line++; + std::cout << "\n"; + } + int lines_up = max_line - lines[this]; + + size_t width = 50; + size_t pct = (100 * current) / total; + size_t pos = (width * current) / total; + + std::cout << "\033[s"; + + if (lines_up > 0) { + std::cout << "\033[" << lines_up << "A"; + } + std::cout << "\033[2K\r[" + << std::string(pos, '=') + << (pos < width ? ">" : "") + << std::string(width - pos, ' ') + << "] " << std::setw(3) << pct << "% (" + << current / (1024 * 1024) << " MB / " + << total / (1024 * 1024) << " MB) " + << "\033[u"; + + std::cout.flush(); + + if (current == total) { + cleanup(this); + } + } + + ProgressBar(const ProgressBar &) = delete; + ProgressBar & operator=(const ProgressBar &) = delete; +}; static bool common_pull_file(httplib::Client & cli, const std::string & resolve_path, @@ -523,6 +568,7 @@ static bool common_pull_file(httplib::Client & cli, const char * func = __func__; // avoid __func__ inside a lambda size_t downloaded = existing_size; size_t progress_step = 0; + ProgressBar bar; auto res = cli.Get(resolve_path, headers, [&](const httplib::Response &response) { @@ -554,7 +600,7 @@ static bool common_pull_file(httplib::Client & cli, progress_step += len; if (progress_step >= total_size / 1000 || downloaded == total_size) { - print_progress(downloaded, total_size); + bar.update(downloaded, total_size); progress_step = 0; } return true; @@ -562,8 +608,6 @@ static bool common_pull_file(httplib::Client & cli, nullptr ); - std::cout << "\n"; - if (!res) { LOG_ERR("%s: error during download. Status: %d\n", __func__, res ? res->status : -1); return false; diff --git a/common/log.cpp b/common/log.cpp index 00a03f158d..b17d2b62c3 100644 --- a/common/log.cpp +++ b/common/log.cpp @@ -420,6 +420,11 @@ void common_log_set_timestamps(struct common_log * log, bool timestamps) { log->set_timestamps(timestamps); } +void common_log_flush(struct common_log * log) { + log->pause(); + log->resume(); +} + static int common_get_verbosity(enum ggml_log_level level) { switch (level) { case GGML_LOG_LEVEL_DEBUG: return LOG_LEVEL_DEBUG; diff --git a/common/log.h b/common/log.h index b24f5f000a..f0f8471b5f 100644 --- a/common/log.h +++ b/common/log.h @@ -84,6 +84,7 @@ void common_log_set_file (struct common_log * log, const char * file); // n void common_log_set_colors (struct common_log * log, log_colors colors); // not thread-safe void common_log_set_prefix (struct common_log * log, bool prefix); // whether to output prefix to each log void common_log_set_timestamps(struct common_log * log, bool timestamps); // whether to output timestamps in the prefix +void common_log_flush (struct common_log * log); // flush all pending log messages // helper macros for logging // use these to avoid computing log arguments if the verbosity of the log is higher than the threshold diff --git a/common/preset.cpp b/common/preset.cpp new file mode 100644 index 0000000000..60746aad58 --- /dev/null +++ b/common/preset.cpp @@ -0,0 +1,206 @@ +#include "arg.h" +#include "preset.h" +#include "peg-parser.h" +#include "log.h" + +#include +#include +#include + +static std::string rm_leading_dashes(const std::string & str) { + size_t pos = 0; + while (pos < str.size() && str[pos] == '-') { + ++pos; + } + return str.substr(pos); +} + +std::vector common_preset::to_args() const { + std::vector args; + + for (const auto & [opt, value] : options) { + args.push_back(opt.args.back()); // use the last arg as the main arg + if (opt.value_hint == nullptr && opt.value_hint_2 == nullptr) { + // flag option, no value + if (common_arg_utils::is_falsey(value)) { + // use negative arg if available + if (!opt.args_neg.empty()) { + args.back() = opt.args_neg.back(); + } else { + // otherwise, skip the flag + // TODO: maybe throw an error instead? + args.pop_back(); + } + } + } + if (opt.value_hint != nullptr) { + // single value + args.push_back(value); + } + if (opt.value_hint != nullptr && opt.value_hint_2 != nullptr) { + throw std::runtime_error(string_format( + "common_preset::to_args(): option '%s' has two values, which is not supported yet", + opt.args.back() + )); + } + } + + return args; +} + +std::string common_preset::to_ini() const { + std::ostringstream ss; + + ss << "[" << name << "]\n"; + for (const auto & [opt, value] : options) { + auto espaced_value = value; + string_replace_all(espaced_value, "\n", "\\\n"); + ss << rm_leading_dashes(opt.args.back()) << " = "; + ss << espaced_value << "\n"; + } + ss << "\n"; + + return ss.str(); +} + +static std::map> parse_ini_from_file(const std::string & path) { + std::map> parsed; + + if (!std::filesystem::exists(path)) { + throw std::runtime_error("preset file does not exist: " + path); + } + + std::ifstream file(path); + if (!file.good()) { + throw std::runtime_error("failed to open server preset file: " + path); + } + + std::string contents((std::istreambuf_iterator(file)), std::istreambuf_iterator()); + + static const auto parser = build_peg_parser([](auto & p) { + // newline ::= "\r\n" / "\n" / "\r" + auto newline = p.rule("newline", p.literal("\r\n") | p.literal("\n") | p.literal("\r")); + + // ws ::= [ \t]* + auto ws = p.rule("ws", p.chars("[ \t]", 0, -1)); + + // comment ::= [;#] (!newline .)* + auto comment = p.rule("comment", p.chars("[;#]", 1, 1) + p.zero_or_more(p.negate(newline) + p.any())); + + // eol ::= ws comment? (newline / EOF) + auto eol = p.rule("eol", ws + p.optional(comment) + (newline | p.end())); + + // ident ::= [a-zA-Z_] [a-zA-Z0-9_.-]* + auto ident = p.rule("ident", p.chars("[a-zA-Z_]", 1, 1) + p.chars("[a-zA-Z0-9_.-]", 0, -1)); + + // value ::= (!eol-start .)* + auto eol_start = p.rule("eol-start", ws + (p.chars("[;#]", 1, 1) | newline | p.end())); + auto value = p.rule("value", p.zero_or_more(p.negate(eol_start) + p.any())); + + // header-line ::= "[" ws ident ws "]" eol + auto header_line = p.rule("header-line", "[" + ws + p.tag("section-name", p.chars("[^]]")) + ws + "]" + eol); + + // kv-line ::= ident ws "=" ws value eol + auto kv_line = p.rule("kv-line", p.tag("key", ident) + ws + "=" + ws + p.tag("value", value) + eol); + + // comment-line ::= ws comment (newline / EOF) + auto comment_line = p.rule("comment-line", ws + comment + (newline | p.end())); + + // blank-line ::= ws (newline / EOF) + auto blank_line = p.rule("blank-line", ws + (newline | p.end())); + + // line ::= header-line / kv-line / comment-line / blank-line + auto line = p.rule("line", header_line | kv_line | comment_line | blank_line); + + // ini ::= line* EOF + auto ini = p.rule("ini", p.zero_or_more(line) + p.end()); + + return ini; + }); + + common_peg_parse_context ctx(contents); + const auto result = parser.parse(ctx); + if (!result.success()) { + throw std::runtime_error("failed to parse server config file: " + path); + } + + std::string current_section = COMMON_PRESET_DEFAULT_NAME; + std::string current_key; + + ctx.ast.visit(result, [&](const auto & node) { + if (node.tag == "section-name") { + const std::string section = std::string(node.text); + current_section = section; + parsed[current_section] = {}; + } else if (node.tag == "key") { + const std::string key = std::string(node.text); + current_key = key; + } else if (node.tag == "value" && !current_key.empty() && !current_section.empty()) { + parsed[current_section][current_key] = std::string(node.text); + current_key.clear(); + } + }); + + return parsed; +} + +static std::map get_map_key_opt(common_params_context & ctx_params) { + std::map mapping; + for (const auto & opt : ctx_params.options) { + for (const auto & env : opt.get_env()) { + mapping[env] = opt; + } + for (const auto & arg : opt.get_args()) { + mapping[rm_leading_dashes(arg)] = opt; + } + } + return mapping; +} + +static bool is_bool_arg(const common_arg & arg) { + return !arg.args_neg.empty(); +} + +static std::string parse_bool_arg(const common_arg & arg, const std::string & key, const std::string & value) { + // if this is a negated arg, we need to reverse the value + for (const auto & neg_arg : arg.args_neg) { + if (rm_leading_dashes(neg_arg) == key) { + return common_arg_utils::is_truthy(value) ? "false" : "true"; + } + } + // otherwise, not negated + return value; +} + +common_presets common_presets_load(const std::string & path, common_params_context & ctx_params) { + common_presets out; + auto key_to_opt = get_map_key_opt(ctx_params); + auto ini_data = parse_ini_from_file(path); + + for (auto section : ini_data) { + common_preset preset; + if (section.first.empty()) { + preset.name = COMMON_PRESET_DEFAULT_NAME; + } else { + preset.name = section.first; + } + LOG_DBG("loading preset: %s\n", preset.name.c_str()); + for (const auto & [key, value] : section.second) { + LOG_DBG("option: %s = %s\n", key.c_str(), value.c_str()); + if (key_to_opt.find(key) != key_to_opt.end()) { + auto & opt = key_to_opt[key]; + if (is_bool_arg(opt)) { + preset.options[opt] = parse_bool_arg(opt, key, value); + } else { + preset.options[opt] = value; + } + LOG_DBG("accepted option: %s = %s\n", key.c_str(), preset.options[opt].c_str()); + } else { + // TODO: maybe warn about unknown key? + } + } + out[preset.name] = preset; + } + + return out; +} diff --git a/common/preset.h b/common/preset.h new file mode 100644 index 0000000000..dceb849eb8 --- /dev/null +++ b/common/preset.h @@ -0,0 +1,32 @@ +#pragma once + +#include "common.h" +#include "arg.h" + +#include +#include +#include + +// +// INI preset parser and writer +// + +constexpr const char * COMMON_PRESET_DEFAULT_NAME = "default"; + +struct common_preset { + std::string name; + // TODO: support repeated args in the future + std::map options; + + // convert preset to CLI argument list + std::vector to_args() const; + + // convert preset to INI format string + std::string to_ini() const; + + // TODO: maybe implement to_env() if needed +}; + +// interface for multiple presets in one file +using common_presets = std::map; +common_presets common_presets_load(const std::string & path, common_params_context & ctx_params); diff --git a/common/sampling.cpp b/common/sampling.cpp index 7a6b7be1e0..6935d84e22 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -104,9 +104,10 @@ struct ring_buffer { struct common_sampler { common_params_sampling params; - struct llama_sampler * grmr; struct llama_sampler * chain; + bool grammar; + ring_buffer prev; std::vector cur; @@ -116,7 +117,6 @@ struct common_sampler { void reset() { prev.clear(); - llama_sampler_reset(grmr); llama_sampler_reset(chain); } @@ -167,10 +167,15 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co lparams.no_perf = params.no_perf; - struct llama_sampler * grmr; + llama_sampler * chain = llama_sampler_chain_init(lparams); + + bool grammar = false; + std::vector samplers; + if (params.grammar.compare(0, 11, "%llguidance") == 0) { #ifdef LLAMA_USE_LLGUIDANCE - grmr = llama_sampler_init_llg(vocab, "lark", params.grammar.c_str()); + samplers.push_back(llama_sampler_init_llg(vocab, "lark", params.grammar.c_str())); + grammar = true; #else GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled"); #endif // LLAMA_USE_LLGUIDANCE @@ -217,30 +222,23 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co trigger_patterns_c.push_back(regex.c_str()); } - grmr = params.grammar_lazy - ? llama_sampler_init_grammar_lazy_patterns(vocab, params.grammar.c_str(), "root", - trigger_patterns_c.data(), trigger_patterns_c.size(), - trigger_tokens.data(), trigger_tokens.size()) - : llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root"); - if (!grmr) { - return nullptr; + if (!params.grammar.empty()) { + if (params.grammar_lazy) { + samplers.push_back( + llama_sampler_init_grammar_lazy_patterns(vocab, params.grammar.c_str(), "root", + trigger_patterns_c.data(), trigger_patterns_c.size(), + trigger_tokens.data(), trigger_tokens.size())); + } else { + samplers.push_back(llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root")); + } + + grammar = true; } } - auto * result = new common_sampler { - /* .params = */ params, - /* .grmr = */ grmr, - /* .chain = */ llama_sampler_chain_init(lparams), - /* .prev = */ ring_buffer(std::max(32, params.n_prev)), - /* .cur = */ {}, - /* .cur_p = */ {}, - }; - - llama_sampler_chain_add(result->chain, - llama_sampler_init_logit_bias( - llama_vocab_n_tokens(vocab), - params.logit_bias.size(), - params.logit_bias.data())); + if (params.has_logit_bias()) { + samplers.push_back(llama_sampler_init_logit_bias(llama_vocab_n_tokens(vocab), params.logit_bias.size(), params.logit_bias.data())); + } if (params.mirostat == 0) { for (const auto & cnstr : params.samplers) { @@ -253,58 +251,70 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co c_breakers.push_back(str.c_str()); } - llama_sampler_chain_add(result->chain, llama_sampler_init_dry (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size())); + samplers.push_back(llama_sampler_init_dry (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size())); } break; case COMMON_SAMPLER_TYPE_TOP_K: - llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k)); + samplers.push_back(llama_sampler_init_top_k (params.top_k)); break; case COMMON_SAMPLER_TYPE_TOP_P: - llama_sampler_chain_add(result->chain, llama_sampler_init_top_p (params.top_p, params.min_keep)); + samplers.push_back(llama_sampler_init_top_p (params.top_p, params.min_keep)); break; case COMMON_SAMPLER_TYPE_TOP_N_SIGMA: - llama_sampler_chain_add(result->chain, llama_sampler_init_top_n_sigma (params.top_n_sigma)); + samplers.push_back(llama_sampler_init_top_n_sigma(params.top_n_sigma)); break; case COMMON_SAMPLER_TYPE_MIN_P: - llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep)); + samplers.push_back(llama_sampler_init_min_p (params.min_p, params.min_keep)); break; case COMMON_SAMPLER_TYPE_XTC: - llama_sampler_chain_add(result->chain, llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed)); + samplers.push_back(llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed)); break; case COMMON_SAMPLER_TYPE_TYPICAL_P: - llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep)); + samplers.push_back(llama_sampler_init_typical (params.typ_p, params.min_keep)); break; case COMMON_SAMPLER_TYPE_TEMPERATURE: - llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent)); + samplers.push_back(llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent)); break; case COMMON_SAMPLER_TYPE_INFILL: - llama_sampler_chain_add(result->chain, llama_sampler_init_infill (vocab)); + samplers.push_back(llama_sampler_init_infill (vocab)); break; case COMMON_SAMPLER_TYPE_PENALTIES: - llama_sampler_chain_add(result->chain, llama_sampler_init_penalties (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present)); + samplers.push_back(llama_sampler_init_penalties (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present)); break; default: GGML_ASSERT(false && "unknown sampler type"); } } - llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed)); + + samplers.push_back(llama_sampler_init_dist(params.seed)); } else if (params.mirostat == 1) { - llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); - llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100)); + samplers.push_back(llama_sampler_init_temp(params.temp)); + samplers.push_back(llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100)); } else if (params.mirostat == 2) { - llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); - llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta)); + samplers.push_back(llama_sampler_init_temp(params.temp)); + samplers.push_back(llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta)); } else { GGML_ASSERT(false && "unknown mirostat version"); } + for (auto * smpl : samplers) { + llama_sampler_chain_add(chain, smpl); + } + + auto * result = new common_sampler { + /* .params = */ params, + /* .chain = */ chain, + /* .grammar = */ grammar, + /* .prev = */ ring_buffer(std::max(32, params.n_prev)), + /* .cur = */ {}, + /* .cur_p = */ {}, + }; + return result; } void common_sampler_free(struct common_sampler * gsmpl) { if (gsmpl) { - llama_sampler_free(gsmpl->grmr); - llama_sampler_free(gsmpl->chain); delete gsmpl; @@ -314,11 +324,24 @@ void common_sampler_free(struct common_sampler * gsmpl) { void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) { const auto tm = gsmpl->tm(); - if (accept_grammar) { - llama_sampler_accept(gsmpl->grmr, token); - } + if (gsmpl->grammar) { + const int n_smpl = llama_sampler_chain_n(gsmpl->chain); - llama_sampler_accept(gsmpl->chain, token); + for (int i = 0; i < n_smpl; i++) { + auto * smpl = llama_sampler_chain_get(gsmpl->chain, i); + + // the grammar sampler is always the first one + if (i == 0) { + if (accept_grammar) { + llama_sampler_accept(smpl, token); + } + } else { + llama_sampler_accept(smpl, token); + } + } + } else { + llama_sampler_accept(gsmpl->chain, token); + } gsmpl->prev.push_back(token); } @@ -329,12 +352,12 @@ void common_sampler_reset(struct common_sampler * gsmpl) { struct common_sampler * common_sampler_clone(common_sampler * gsmpl) { return new common_sampler { - /* .params = */ gsmpl->params, - /* .grmr = */ llama_sampler_clone(gsmpl->grmr), - /* .chain = */ llama_sampler_clone(gsmpl->chain), - /* .prev = */ gsmpl->prev, - /* .cur = */ gsmpl->cur, - /* .cur_p = */ gsmpl->cur_p, + /* .params = */ gsmpl->params, + /* .chain = */ llama_sampler_clone(gsmpl->chain), + /* .grammar = */ gsmpl->grammar, + /* .prev = */ gsmpl->prev, + /* .cur = */ gsmpl->cur, + /* .cur_p = */ gsmpl->cur_p, }; } @@ -383,58 +406,33 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam } } -llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) { +struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl) { + return gsmpl->chain; +} + +llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx) { llama_synchronize(ctx); // start measuring sampling time after the llama_context synchronization in order to not measure any ongoing async operations const auto tm = gsmpl->tm(); - gsmpl->set_logits(ctx, idx); + llama_token id = LLAMA_TOKEN_NULL; - auto & grmr = gsmpl->grmr; auto & chain = gsmpl->chain; auto & cur_p = gsmpl->cur_p; // initialized by set_logits - if (grammar_first) { - llama_sampler_apply(grmr, &cur_p); - } + gsmpl->set_logits(ctx, idx); llama_sampler_apply(chain, &cur_p); GGML_ASSERT(cur_p.selected != -1 && "no selected token during sampling - check your sampling configuration"); - const llama_token id = cur_p.data[cur_p.selected].id; + id = cur_p.data[cur_p.selected].id; - if (grammar_first) { - return id; - } - - // check if it the sampled token fits the grammar - { - llama_token_data single_token_data = { id, 1.0f, 0.0f }; - llama_token_data_array single_token_data_array = { &single_token_data, 1, -1, false }; - - llama_sampler_apply(grmr, &single_token_data_array); - - const bool is_valid = single_token_data_array.data[0].logit != -INFINITY; - if (is_valid) { - return id; - } - } - - // resampling: - // if the token is not valid, sample again, but first apply the grammar sampler and then the sampling chain - gsmpl->set_logits(ctx, idx); - - llama_sampler_apply(grmr, &cur_p); - llama_sampler_apply(chain, &cur_p); - - GGML_ASSERT(cur_p.selected != -1 && "no selected token during re-sampling - check your sampling configuration"); - - return cur_p.data[cur_p.selected].id; + return id; } -std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector & idxs, const llama_tokens & draft, bool grammar_first) { +std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector & idxs, const llama_tokens & draft) { GGML_ASSERT(idxs.size() == draft.size() + 1 && "idxs.size() must be draft.size() + 1"); std::vector result; @@ -442,7 +440,7 @@ std::vector common_sampler_sample_and_accept_n(struct common_sample size_t i = 0; for (; i < draft.size(); i++) { - const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first); + const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i]); common_sampler_accept(gsmpl, id, true); @@ -454,7 +452,7 @@ std::vector common_sampler_sample_and_accept_n(struct common_sample } if (i == draft.size()) { - const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first); + const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i]); common_sampler_accept(gsmpl, id, true); @@ -464,13 +462,13 @@ std::vector common_sampler_sample_and_accept_n(struct common_sample return result; } -std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first) { +std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft) { std::vector idxs(draft.size() + 1); for (size_t i = 0; i < idxs.size(); ++i) { idxs[i] = i; } - return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft, grammar_first); + return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft); } uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) { @@ -515,7 +513,8 @@ std::string common_sampler_print(const struct common_sampler * gsmpl) { for (int i = 0; i < llama_sampler_chain_n(gsmpl->chain); i++) { const auto * smpl = llama_sampler_chain_get(gsmpl->chain, i); - result += std::string("-> ") + llama_sampler_name(smpl) + " "; + result += std::string("-> "); + result += std::string(llama_sampler_name(smpl)) + " "; } return result; diff --git a/common/sampling.h b/common/sampling.h index e198eecda3..ace5d3d020 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -48,6 +48,8 @@ struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl); // arguments can be nullptr to skip printing void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl); +struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl); + // extended sampling implementation: // // - set logits @@ -55,10 +57,7 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam // - check if the token fits the grammar (if any) // - if not: resample by first applying the grammar constraints and then sampling again (slower path) // -// if grammar_first is true, the grammar is applied before the samplers (slower) -// useful in cases where all the resulting candidates (not just the sampled one) must fit the grammar -// -llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false); +llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx); // generalized version of common_sampler_sample // @@ -76,10 +75,10 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co // // returns at least 1 token, up to idxs.size() // -std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector & idxs, const llama_tokens & draft, bool grammar_first = false); +std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector & idxs, const llama_tokens & draft); // assume idxs == [ 0, 1, 2, ..., draft.size() ] -std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first = false); +std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft); uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl); @@ -107,3 +106,9 @@ std::vector common_sampler_types_from_chars(const std: llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab, const char * grammar_kind, const char * grammar_data); + +struct common_sampler_deleter { + void operator()(common_sampler * s) { common_sampler_free(s); } +}; + +typedef std::unique_ptr common_sampler_ptr; diff --git a/common/speculative.cpp b/common/speculative.cpp index 3e83b0964c..1e12383ae6 100644 --- a/common/speculative.cpp +++ b/common/speculative.cpp @@ -315,7 +315,7 @@ llama_tokens common_speculative_gen_draft( for (int i = 0; i < params.n_draft; ++i) { common_batch_clear(batch); - common_sampler_sample(smpl, ctx_dft, 0, true); + common_sampler_sample(smpl, ctx_dft, 0); const auto * cur_p = common_sampler_get_candidates(smpl, true); diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index c641989c19..e1c78e3b18 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -383,6 +383,17 @@ class ModelBase: s = self.model_tensors[name] self.model_tensors[weight_name] = lambda w=w, s=s, bs=block_size: dequant_simple(w(), s(), bs) tensors_to_remove.append(name) + if name.endswith(".activation_scale"): # unused + tensors_to_remove.append(name) + # mistral format + if name.endswith(".qscale_weight"): + weight_name = name.removesuffix("qscale_weight") + "weight" + w = self.model_tensors[weight_name] + s = self.model_tensors[name] + self.model_tensors[weight_name] = lambda w=w, s=s, bs=block_size: dequant_simple(w(), s(), bs) + tensors_to_remove.append(name) + if name.endswith(".qscale_act"): + tensors_to_remove.append(name) elif quant_method == "gptq": for name in self.model_tensors.keys(): if name.endswith(".qweight"): @@ -694,6 +705,9 @@ class ModelBase: if "llm_config" in config: # rename for InternVL config["text_config"] = config["llm_config"] + if "lm_config" in config: + # rename for GlmASR + config["text_config"] = config["lm_config"] if "thinker_config" in config: # rename for Qwen2.5-Omni config["text_config"] = config["thinker_config"]["text_config"] @@ -743,6 +757,15 @@ class TextModel(ModelBase): self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers"]) self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) + self.rope_parameters = self.hparams.get("rope_parameters", self.hparams.get("rope_scaling")) or {} + + # Ensure "rope_theta" and "rope_type" is mirrored in rope_parameters + if "full_attention" not in self.rope_parameters and "sliding_attention" not in self.rope_parameters: + if "rope_theta" not in self.rope_parameters and (rope_theta := self.find_hparam(["rope_theta", "global_rope_theta", "rotary_emb_base"], optional=True)) is not None: + self.rope_parameters["rope_theta"] = rope_theta + if "rope_type" not in self.rope_parameters and (rope_type := self.rope_parameters.get("type")) is not None: + self.rope_parameters["rope_type"] = rope_type + @classmethod def __init_subclass__(cls): # can't use an abstract property, because overriding it without type errors @@ -784,7 +807,7 @@ class TextModel(ModelBase): def set_gguf_parameters(self): self.gguf_writer.add_block_count(self.block_count) - if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions", "max_length"], optional=True)) is not None: + if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions", "max_length", "max_sequence_length", "model_max_length"], optional=True)) is not None: self.gguf_writer.add_context_length(n_ctx) logger.info(f"gguf: context length = {n_ctx}") @@ -804,7 +827,42 @@ class TextModel(ModelBase): self.gguf_writer.add_head_count_kv(n_head_kv) logger.info(f"gguf: key-value head count = {n_head_kv}") - if (rope_theta := self.hparams.get("rope_theta")) is not None: + rope_params = self.rope_parameters.get("full_attention", self.rope_parameters) + if (rope_type := rope_params.get("rope_type")) is not None: + rope_factor = rope_params.get("factor") + rope_gguf_type = gguf.RopeScalingType.NONE + if rope_type == "linear" and rope_factor is not None: + rope_gguf_type = gguf.RopeScalingType.LINEAR + self.gguf_writer.add_rope_scaling_type(rope_gguf_type) + self.gguf_writer.add_rope_scaling_factor(rope_factor) + elif rope_type == "yarn" and rope_factor is not None: + rope_gguf_type = gguf.RopeScalingType.YARN + self.gguf_writer.add_rope_scaling_type(rope_gguf_type) + self.gguf_writer.add_rope_scaling_factor(rope_factor) + self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_params["original_max_position_embeddings"]) + if (yarn_ext_factor := rope_params.get("extrapolation_factor")) is not None: + self.gguf_writer.add_rope_scaling_yarn_ext_factor(yarn_ext_factor) + if (yarn_attn_factor := rope_params.get("attention_factor", rope_params.get("attn_factor"))) is not None: + self.gguf_writer.add_rope_scaling_yarn_attn_factor(yarn_attn_factor) + if (yarn_beta_fast := rope_params.get("beta_fast")) is not None: + self.gguf_writer.add_rope_scaling_yarn_beta_fast(yarn_beta_fast) + if (yarn_beta_slow := rope_params.get("beta_slow")) is not None: + self.gguf_writer.add_rope_scaling_yarn_beta_slow(yarn_beta_slow) + # self.gguf_writer.add_rope_scaling_yarn_log_mul(rope_params["mscale_all_dim"]) + elif rope_type == "su" or rope_type == "longrope": + rope_gguf_type = gguf.RopeScalingType.LONGROPE + self.gguf_writer.add_rope_scaling_type(rope_gguf_type) + elif rope_type == "dynamic": + # HunYuan, handled in model class + pass + elif rope_type.lower() == "llama3": + # Handled in generate_extra_tensors + pass + else: + logger.warning(f"Unknown RoPE type: {rope_type}") + logger.info(f"gguf: rope scaling type = {rope_gguf_type.name}") + + if (rope_theta := rope_params.get("rope_theta")) is not None: self.gguf_writer.add_rope_freq_base(rope_theta) logger.info(f"gguf: rope theta = {rope_theta}") if (f_rms_eps := self.find_hparam(["rms_norm_eps", "norm_eps"], optional=True)) is not None: @@ -1146,6 +1204,9 @@ class TextModel(ModelBase): if chkhsh == "f4f37b6c8eb9ea29b3eac6bb8c8487c5ab7885f8d8022e67edc1c68ce8403e95": # ref: https://huggingface.co/MiniMaxAI/MiniMax-M2 res = "minimax-m2" + if chkhsh == "4a2e2abae11ca2b86d570fc5b44be4d5eb5e72cc8f22dd136a94b37da83ab665": + # ref: https://huggingface.co/KORMo-Team/KORMo-tokenizer + res = "kormo" if res is None: logger.warning("\n") @@ -1475,6 +1536,21 @@ class TextModel(ModelBase): raise NotImplementedError("Only MEAN, CLS, and LAST pooling types supported") self.gguf_writer.add_pooling_type(pooling_type) + def _set_vocab_glmedge(self): + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(self.dir_model) + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True) + tokens, toktypes, tokpre = self.get_vocab_base() + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"]) + special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"]) + special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"]) + special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|endoftext|>"]) + special_vocab.add_to_gguf(self.gguf_writer) + def _set_vocab_interns1(self): tokens: list[str] = [] toktypes: list[int] = [] @@ -1604,7 +1680,7 @@ class MmprojModel(ModelBase): preprocessor_config: dict[str, Any] global_config: dict[str, Any] - n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth"] + n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth", "encoder_layers"] has_vision_encoder: bool = True # by default has_audio_encoder: bool = False @@ -1680,7 +1756,8 @@ class MmprojModel(ModelBase): return self.global_config.get(config_name) def get_audio_config(self) -> dict[str, Any] | None: - return self.global_config.get("audio_config") + mm_config_key = "whisper_config" if "whisper_config" in self.hparams else "audio_config" + return self.global_config.get(mm_config_key) def set_type(self): self.gguf_writer.add_type(gguf.GGUFType.MMPROJ) @@ -1955,34 +2032,10 @@ class BaichuanModel(TextModel): self._set_vocab_sentencepiece() def set_gguf_parameters(self): - head_count = self.hparams["num_attention_heads"] - head_count_kv = self.hparams.get("num_key_value_heads", head_count) - - ctx_length = 0 - if "max_sequence_length" in self.hparams: - ctx_length = self.hparams["max_sequence_length"] - elif "max_position_embeddings" in self.hparams: - ctx_length = self.hparams["max_position_embeddings"] - elif "model_max_length" in self.hparams: - ctx_length = self.hparams["model_max_length"] - else: - raise ValueError("gguf: can not find ctx length parameter.") + super().set_gguf_parameters() self.gguf_writer.add_tensor_data_layout("Meta AI original pth") - self.gguf_writer.add_context_length(ctx_length) - self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) - self.gguf_writer.add_block_count(self.block_count) - self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) - self.gguf_writer.add_head_count(head_count) - self.gguf_writer.add_head_count_kv(head_count_kv) - self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) - self.gguf_writer.add_file_type(self.ftype) - - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: head_count = self.hparams["num_attention_heads"] @@ -2078,34 +2131,10 @@ class XverseModel(TextModel): special_vocab.add_to_gguf(self.gguf_writer) def set_gguf_parameters(self): - head_count = self.hparams["num_attention_heads"] - head_count_kv = self.hparams.get("num_key_value_heads", head_count) - - ctx_length = 0 - if "max_sequence_length" in self.hparams: - ctx_length = self.hparams["max_sequence_length"] - elif "max_position_embeddings" in self.hparams: - ctx_length = self.hparams["max_position_embeddings"] - elif "model_max_length" in self.hparams: - ctx_length = self.hparams["model_max_length"] - else: - raise ValueError("gguf: can not find ctx length parameter.") + super().set_gguf_parameters() self.gguf_writer.add_tensor_data_layout("Meta AI original pth") - self.gguf_writer.add_context_length(ctx_length) - self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) - self.gguf_writer.add_block_count(self.block_count) - self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) - self.gguf_writer.add_head_count(head_count) - self.gguf_writer.add_head_count_kv(head_count_kv) - self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) - self.gguf_writer.add_file_type(self.ftype) - - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused @@ -2366,8 +2395,13 @@ class LlamaModel(TextModel): # fix for SmolVLM2, missing `num_attention_heads` in config.json if self.hf_arch == "VLlama3ForCausalLM": self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 32) + hparams = ModelBase.load_hparams(self.dir_model, is_mistral_format=False) + self.origin_hf_arch = hparams.get('architectures', [None])[0] def set_vocab(self): + if self.origin_hf_arch == "GlmasrModel": + return self._set_vocab_glmedge() + if self.is_mistral_format: return self._set_vocab_mistral() @@ -2419,11 +2453,6 @@ class LlamaModel(TextModel): rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] self.gguf_writer.add_rope_dimension_count(rope_dim) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - @staticmethod def permute(weights: Tensor, n_head: int, n_head_kv: int | None): if n_head_kv is not None and n_head != n_head_kv: @@ -2443,6 +2472,7 @@ class LlamaModel(TextModel): "vision_language_adapter.", "patch_merger.", "pre_mm_projector_norm", + "audio_encoder.", ] is_multimodal_tensor = "vision_tower" in name \ @@ -2507,16 +2537,16 @@ class LlamaModel(TextModel): return [(self.map_tensor_name(name), data_torch)] def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: - if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): - if rope_scaling.get("rope_type", '').lower() == "llama3": - base = self.hparams.get("rope_theta", 10000.0) + if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters): + if rope_params.get("rope_type", '').lower() == "llama3": + base = rope_params.get("rope_theta", 10000.0) if (dim := self.hparams.get("head_dim")) is None: dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) - factor = rope_scaling.get("factor", 8.0) - low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) - high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + factor = rope_params.get("factor", 8.0) + low_freq_factor = rope_params.get("low_freq_factor", 1.0) + high_freq_factor = rope_params.get("high_freq_factor", 4.0) old_context_len = self.hparams.get("original_max_position_embeddings", 8192) low_freq_wavelen = old_context_len / low_freq_factor @@ -2553,11 +2583,6 @@ class ArceeModel(LlamaModel): def set_gguf_parameters(self): super().set_gguf_parameters() self._try_set_pooling_type() - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) @ModelBase.register("AfmoeForCausalLM") @@ -2840,27 +2865,18 @@ class Mistral3Model(LlamaModel): def set_gguf_parameters(self): super().set_gguf_parameters() - rope_params = self.hparams.get("rope_parameters") + rope_params = self.rope_parameters if self.hparams.get("model_type") == "ministral3": - assert rope_params is not None, "ministral3 must have 'rope_parameters' config" + assert rope_params, "ministral3 must have 'rope_parameters' config" assert rope_params["rope_type"] == "yarn", "ministral3 rope_type must be 'yarn'" - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_params["factor"]) - self.gguf_writer.add_rope_scaling_yarn_beta_fast(rope_params["beta_fast"]) - self.gguf_writer.add_rope_scaling_yarn_beta_slow(rope_params["beta_slow"]) self.gguf_writer.add_rope_scaling_yarn_log_mul(rope_params["mscale_all_dim"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_params["original_max_position_embeddings"]) - self.gguf_writer.add_rope_freq_base(rope_params["rope_theta"]) self.gguf_writer.add_attn_temperature_scale(rope_params["llama_4_scaling_beta"]) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None): - # TODO: probably not worth supporting quantized weight, as official BF16 is also available - if name.endswith("weight_scale_inv"): - raise ValueError("This is a quantized weight, please use BF16 weight instead") - name = name.replace("language_model.", "") if "multi_modal_projector" in name or "vision_tower" in name: return [] + return super().modify_tensors(data_torch, name, bid) @@ -2950,7 +2966,7 @@ class DeciModel(TextModel): assert self.block_count == len(self._num_kv_heads) assert self.block_count == len(self._num_heads) assert self.block_count == len(self._ffn_dims) - if (rope_theta := self.hparams.get("rope_theta")) is not None: + if (rope_theta := self.rope_parameters.get("rope_theta")) is not None: self.gguf_writer.add_rope_freq_base(rope_theta) self.gguf_writer.add_head_count_kv(self._num_kv_heads) self.gguf_writer.add_head_count(self._num_heads) @@ -2975,11 +2991,6 @@ class DeciModel(TextModel): rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] self.gguf_writer.add_rope_dimension_count(rope_dim) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - @staticmethod def permute(weights: Tensor, n_head: int, n_head_kv: int | None): if n_head_kv is not None and n_head != n_head_kv: @@ -3008,16 +3019,16 @@ class DeciModel(TextModel): return [(self.map_tensor_name(name), data_torch)] def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: - if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): - if rope_scaling.get("rope_type", '').lower() == "llama3": - base = self.hparams.get("rope_theta", 10000.0) + if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters): + if rope_params.get("rope_type", '').lower() == "llama3": + base = rope_params.get("rope_theta", 10000.0) if (dim := self.hparams.get("head_dim")) is None: dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) - factor = rope_scaling.get("factor", 8.0) - low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) - high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + factor = rope_params.get("factor", 8.0) + low_freq_factor = rope_params.get("low_freq_factor", 1.0) + high_freq_factor = rope_params.get("high_freq_factor", 4.0) old_context_len = self.hparams.get("original_max_position_embeddings", 8192) low_freq_wavelen = old_context_len / low_freq_factor @@ -3271,10 +3282,6 @@ class MiniCPMModel(TextModel): logit_scale = self.hparams["hidden_size"] / self.hparams["dim_model_base"] self.gguf_writer.add_logit_scale(logit_scale) logger.info(f"gguf: (minicpm) logit_scale = {logit_scale}") - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "longrope": - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LONGROPE) - logger.info(f"gguf: (minicpm) rope_scaling_type = {gguf.RopeScalingType.LONGROPE}") def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: rope_dims = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] @@ -3394,19 +3401,8 @@ class QwenModel(TextModel): def set_vocab(self): self._set_vocab_qwen() - def set_gguf_parameters(self): - self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) - self.gguf_writer.add_block_count(self.block_count) - self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) - self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) - self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"]) - self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) - self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) - self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) - self.gguf_writer.add_file_type(self.ftype) - -@ModelBase.register("Qwen2Model", "Qwen2ForCausalLM", "Qwen2AudioForConditionalGeneration") +@ModelBase.register("Qwen2Model", "Qwen2ForCausalLM", "Qwen2AudioForConditionalGeneration", "KORMoForCausalLM") class Qwen2Model(TextModel): model_arch = gguf.MODEL_ARCH.QWEN2 @@ -3419,11 +3415,6 @@ class Qwen2Model(TextModel): def set_gguf_parameters(self): super().set_gguf_parameters() self._try_set_pooling_type() - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: if self.hf_arch == "Qwen2Model": @@ -3491,12 +3482,6 @@ class DreamModel(TextModel): # Dream models use non-causal attention for diffusion self.gguf_writer.add_causal_attention(False) - # Handle RoPE scaling similar to Qwen2 - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) # Add Dream-specific parameters mask_token_id = self.hparams.get("mask_token_id") @@ -4040,13 +4025,6 @@ class Qwen2MoeModel(TextModel): if (shared_expert_intermediate_size := self.hparams.get('shared_expert_intermediate_size')) is not None: self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size) logger.info(f"gguf: expert shared feed forward length = {shared_expert_intermediate_size}") - # YaRN is not enabled by default - # To enable it, please refer to this guide: https://huggingface.co/Qwen/Qwen3-30B-A3B#processing-long-texts - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) _experts: list[dict[str, Tensor]] | None = None @@ -4648,7 +4626,7 @@ class Phi3MiniModel(TextModel): self.gguf_writer.add_head_count_kv(n_head_kv) self.gguf_writer.add_layer_norm_rms_eps(rms_eps) self.gguf_writer.add_rope_dimension_count(rope_dims) - self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"])) + self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("full_attention", self.rope_parameters)["rope_theta"]) self.gguf_writer.add_file_type(self.ftype) sliding_window = self.hparams.get("sliding_window") # use zero value of sliding_window to distinguish Phi-4 from other PHI3 models @@ -4924,7 +4902,7 @@ class Plamo2Model(TextModel): self.gguf_writer.add_value_length(hparams.get("hidden_size_per_head", 128)) self.gguf_writer.add_block_count(self.block_count) self.gguf_writer.add_layer_norm_rms_eps(hparams.get("rms_norm_eps", 1e-06)) - self.gguf_writer.add_rope_freq_base(hparams.get("rope_theta", 10000)) + self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("rope_theta", 10000)) # Mamba parameters self.gguf_writer.add_ssm_state_size(hparams.get("mamba_d_state", 64)) @@ -5122,21 +5100,6 @@ class InternLM2Model(TextModel): special_vocab.add_to_gguf(self.gguf_writer) - def set_gguf_parameters(self): - self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) - self.gguf_writer.add_block_count(self.block_count) - self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) - self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) - self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"]) - self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) - self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) - self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"]) - self.gguf_writer.add_file_type(self.ftype) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: num_heads = self.hparams["num_attention_heads"] num_kv_heads = self.hparams["num_key_value_heads"] @@ -5213,11 +5176,6 @@ class InternLM3Model(TextModel): rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] self.gguf_writer.add_rope_dimension_count(rope_dim) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: n_head = self.hparams["num_attention_heads"] n_kv_head = self.hparams.get("num_key_value_heads") @@ -5580,7 +5538,6 @@ class NomicBertModel(BertModel): def set_gguf_parameters(self): super().set_gguf_parameters() - self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"]) if self.is_moe: self.gguf_writer.add_moe_every_n_layers(self.hparams["moe_every_n_layers"]) self.gguf_writer.add_expert_count(self.hparams["num_experts"]) @@ -5703,8 +5660,6 @@ class XLMRobertaModel(BertModel): super().set_gguf_parameters() # jina-embeddings-v3 - if rotary_emb_base := self.hparams.get("rotary_emb_base"): - self.gguf_writer.add_rope_freq_base(rotary_emb_base) lora_alpha = self.hparams.get("lora_alpha") if lora_prompt_prefixes := self.hparams.get("task_instructions"): assert self._lora_files and all(lora_name in lora_prompt_prefixes for lora_name in self._lora_files.keys()) @@ -5825,33 +5780,30 @@ class Gemma3Model(TextModel): norm_shift = 1.0 # Gemma3RMSNorm adds 1.0 to the norm value def set_vocab(self): - self._set_vocab_sentencepiece() - - self.gguf_writer.add_add_space_prefix(False) + if (self.dir_model / "tokenizer.model").is_file(): + self._set_vocab_sentencepiece() + self.gguf_writer.add_add_space_prefix(False) + else: + self._set_vocab_gpt2() def set_gguf_parameters(self): + super().set_gguf_parameters() hparams = self.hparams # some default values are not specified in the hparams self.gguf_writer.add_context_length(hparams.get("max_position_embeddings", 131072)) - self.gguf_writer.add_embedding_length(hparams["hidden_size"]) - self.gguf_writer.add_block_count(self.block_count) - self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) self.gguf_writer.add_head_count(hparams.get("num_attention_heads", 8)) self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-6)) self.gguf_writer.add_key_length(hparams.get("head_dim", 256)) self.gguf_writer.add_value_length(hparams.get("head_dim", 256)) - self.gguf_writer.add_file_type(self.ftype) - self.gguf_writer.add_rope_freq_base(hparams.get("rope_theta", 1_000_000.0)) # for global layers + self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("full_attention", self.rope_parameters).get("rope_theta", 1_000_000.0)) # for global layers # attn_logit_softcapping is removed in Gemma3 assert hparams.get("attn_logit_softcapping") is None - self.gguf_writer.add_sliding_window(hparams["sliding_window"]) + if (final_logit_softcap := hparams.get("final_logit_softcapping")): + self.gguf_writer.add_final_logit_softcapping(final_logit_softcap) + if hparams.get("sliding_window_pattern") != 1: + self.gguf_writer.add_sliding_window(hparams["sliding_window"]) self.gguf_writer.add_head_count_kv(hparams.get("num_key_value_heads", 4)) - if hparams.get("rope_scaling") is not None: - assert hparams["rope_scaling"]["rope_type"] == "linear" - # important: this rope_scaling is only applied for global layers, and not used by 1B model - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(hparams["rope_scaling"]["factor"]) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused @@ -5865,8 +5817,10 @@ class Gemma3Model(TextModel): # remove OOV (out-of-vocabulary) rows in token_embd if "embed_tokens.weight" in name: - vocab = self._create_vocab_sentencepiece() - tokens = vocab[0] + if (self.dir_model / "tokenizer.model").is_file(): + tokens = self._create_vocab_sentencepiece()[0] + else: + tokens = self.get_vocab_base()[0] data_torch = data_torch[:len(tokens)] # ref code in Gemma3RMSNorm @@ -6753,13 +6707,6 @@ class Olmo2Model(TextModel): def set_gguf_parameters(self): super().set_gguf_parameters() - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_attn_factors(rope_scaling["attention_factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) - if "sliding_window" in self.hparams: self.gguf_writer.add_sliding_window(self.hparams["sliding_window"]) @@ -7258,12 +7205,11 @@ class DeepseekV2Model(TextModel): self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"]) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) - self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * rope_scaling["mscale_all_dim"]) + if (rope_mscale_all := self.rope_parameters.get("mscale_all_dim")) is not None: + # [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX] + # note: for legacy reasons, this is not consistent with the other usages of self.gguf_writer.add_rope_scaling_yarn_log_mul + # ref https://github.com/ggml-org/llama.cpp/pull/17945 + self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * rope_mscale_all) _experts: list[dict[str, Tensor]] | None = None @@ -7871,11 +7817,6 @@ class Glm4Model(TextModel): if (rope_dim := self.hparams.get("head_dim")) is None: rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5))) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: if name.startswith("model.visual."): # ignore visual part of Glm4v @@ -8213,50 +8154,26 @@ class ExaoneModel(TextModel): model_arch = gguf.MODEL_ARCH.EXAONE def set_gguf_parameters(self): + super().set_gguf_parameters() hparams = self.hparams assert (hparams["activation_function"] == "silu") - max_position_embeddings = hparams["max_position_embeddings"] - embed_dim = hparams["hidden_size"] - num_heads = hparams["num_attention_heads"] - num_kv_heads = hparams.get("num_key_value_heads", num_heads) - layer_norm_eps = hparams["layer_norm_epsilon"] - intermediate_size = hparams["intermediate_size"] if "intermediate_size" in hparams else 4 * embed_dim - # ignore for now as EXAONE-3.0-7.8B-Instruct attentino_dropout is 0.0 - # attention_dropout_rate = hparams["attention_dropout"] - # ignore for now as EXAONE-3.0-7.8B-Instruct embed_dropout is 0.0 - # embed_dropout_rate = hparams["embed_dropout"] - self.gguf_writer.add_embedding_length(embed_dim) - self.gguf_writer.add_head_count(num_heads) - self.gguf_writer.add_head_count_kv(num_kv_heads) - self.gguf_writer.add_context_length(max_position_embeddings) - self.gguf_writer.add_layer_norm_rms_eps(layer_norm_eps) - self.gguf_writer.add_feed_forward_length(intermediate_size) - self.gguf_writer.add_block_count(self.block_count) - self.gguf_writer.add_file_type(self.ftype) - - if (rope_theta := self.hparams.get("rope_theta")) is not None: - self.gguf_writer.add_rope_freq_base(rope_theta) rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"], optional=True) rotary_factor = rotary_factor if rotary_factor is not None else 1.0 self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"]))) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: - if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): - if rope_scaling.get("rope_type", '').lower() == "llama3": - base = self.hparams.get("rope_theta", 10000.0) + if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters): + if rope_params.get("rope_type", '').lower() == "llama3": + base = self.rope_parameters.get("rope_theta", 10000.0) if (dim := self.hparams.get("head_dim")) is None: dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) - factor = rope_scaling.get("factor", 8.0) - low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) - high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + factor = rope_params.get("factor", 8.0) + low_freq_factor = rope_params.get("low_freq_factor", 1.0) + high_freq_factor = rope_params.get("high_freq_factor", 4.0) old_context_len = self.hparams.get("original_max_position_embeddings", 8192) low_freq_wavelen = old_context_len / low_freq_factor @@ -8311,22 +8228,17 @@ class Exaone4Model(TextModel): if len(sliding_window_pattern) == hparams["num_hidden_layers"]: self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: - if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): - if rope_scaling.get("rope_type", '').lower() == "llama3": - base = self.hparams.get("rope_theta", 10_000.0) + if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters): + if rope_params.get("rope_type", '').lower() == "llama3": + base = rope_params.get("rope_theta", 10_000.0) if (dim := self.hparams.get("head_dim")) is None: dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) - factor = rope_scaling.get("factor", 16.0) - low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) - high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + factor = rope_params.get("factor", 16.0) + low_freq_factor = rope_params.get("low_freq_factor", 1.0) + high_freq_factor = rope_params.get("high_freq_factor", 4.0) old_context_len = self.hparams.get("original_max_position_embeddings", 8192) low_freq_wavelen = old_context_len / low_freq_factor @@ -8637,13 +8549,6 @@ class BailingMoeModel(TextModel): rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] self.gguf_writer.add_rope_dimension_count(rope_dim) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) - else: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"]) self.gguf_writer.add_vocab_size(hparams["vocab_size"]) self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"]) @@ -8750,13 +8655,6 @@ class BailingMoeV2Model(TextModel): rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5))) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) - else: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"]) self.gguf_writer.add_vocab_size(hparams["vocab_size"]) self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"]) @@ -8835,13 +8733,6 @@ class GroveMoeModel(TextModel): self.gguf_writer.add_experts_per_group(2) # FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L376 self.gguf_writer.add_expert_group_scale(0.05) - # YaRN is not enabled by default - # To enable it, please refer to this guide: https://huggingface.co/Qwen/Qwen3-30B-A3B#processing-long-texts - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) _experts: list[dict[str, Tensor]] | None = None _chunk_experts: list[dict[str, Tensor]] | None = None @@ -8984,6 +8875,63 @@ class UltravoxModel(TextModel): raise NotImplementedError("Ultravox does not have text decoder. Instead, it uses Llama or other models for text. If you want to get the audio encoder, please use --mmproj argument") +@ModelBase.register("GlmasrModel") +class GlmASRWhisperEncoderModel(MmprojModel): + has_vision_encoder = False + has_audio_encoder = True + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + if "hidden_size" not in self.hparams and "intermediate_size" not in self.hparams: + self.hparams["hidden_size"] = self.hparams["d_model"] + self.hparams["intermediate_size"] = self.hparams["encoder_ffn_dim"] + self.hparams["num_attention_heads"] = self.hparams["encoder_attention_heads"] + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GLMA) + self.gguf_writer.add_audio_num_mel_bins(self.hparams["num_mel_bins"]) + self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-5)) + self.gguf_writer.add_audio_stack_factor(self.global_config["merge_factor"]) + + def tensor_force_quant(self, name, new_name, bid, n_dims): + if ".conv" in name and ".weight" in name: + return gguf.GGMLQuantizationType.F16 + return super().tensor_force_quant(name, new_name, bid, n_dims) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + if name.startswith("model.") or name.startswith("lm_head."): + # skip language model tensors + return [] + + if name.startswith("audio_encoder.whisper."): + name = name.replace("audio_encoder.whisper.","audio_tower.") + if "audio_encoder.layer_norm." in name or "audio_encoder.proj." in name: + name = name.replace("audio_encoder.", "audio_encoder.adapting.") + + if name.startswith("audio_encoder.audio_bos_eos_token."): + return [(self.map_tensor_name("model.vision.boi"), data_torch[0]), (self.map_tensor_name("model.vision.eoi"), data_torch[1])] + + if name.startswith("audio_encoder.adapting."): + name = name.replace("audio_encoder.adapting.","audio.multi_modal_projector.") + if ".layer_norm." in name: + name = name.replace(".layer_norm.", ".ln_pre.") + if ".0." in name: + name = name.replace(".0.", ".linear_1.") + if ".2." in name: + name = name.replace(".2.", ".linear_2.") + if ".proj." in name: + return [] + + if "conv1.bias" in name or "conv2.bias" in name: + # transpose conv1 and conv2 bias + data_torch = data_torch.unsqueeze(-1) + + return [(self.map_tensor_name(name), data_torch)] + + @ModelBase.register("Qwen2AudioForConditionalGeneration") class WhisperEncoderModel(MmprojModel): has_vision_encoder = False # no vision encoder @@ -9151,7 +9099,7 @@ class FalconH1Model(Mamba2Model): assert self.d_inner % self.d_head == 0, f"SSM inner size {self.d_inner} not a multiple of head dim {self.d_head}" # Add any other Falcon Mamba2 specific configuration - self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"])) + self.gguf_writer.add_rope_freq_base(self.rope_parameters["rope_theta"]) @ModelBase.register("HunYuanMoEV1ForCausalLM") @@ -9229,12 +9177,11 @@ class HunYuanMoEModel(TextModel): self.gguf_writer.add_expert_shared_count(moe_shared_expert[0]) # Rope - rope_scaling = hparams.get("rope_scaling", {}) - if rope_scaling.get("type") == "dynamic": + if self.rope_parameters.get("rope_type") == "dynamic": # HunYuan uses NTK Aware Alpha based scaling. Original implementation: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ # 1000 corresponds to a usable context length of 256k (https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf) - alpha = rope_scaling.get("alpha", 1000) - base = hparams.get("rope_theta", 10000.0) + alpha = self.rope_parameters.get("alpha", 1000) + base = self.rope_parameters.get("rope_theta", 10000.0) dim = (hparams["hidden_size"] // hparams["num_attention_heads"]) # 128 scaled_base = base * (alpha ** (dim / (dim - 2))) # 10000 * (1000 ** (128 / 126)) = 11158839.9251 self.gguf_writer.add_rope_freq_base(scaled_base) @@ -9429,12 +9376,11 @@ class HunYuanModel(TextModel): hparams = self.hparams # Rope - rope_scaling = hparams.get("rope_scaling", {}) - if rope_scaling.get("type") == "dynamic": + if self.rope_parameters.get("rope_type") == "dynamic": # HunYuan uses NTK Aware Alpha based scaling. Original implementation: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ # 1000 corresponds to a usable context length of 256k (https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf) - alpha = rope_scaling.get("alpha", 50) - base = hparams.get("rope_theta", 10000.0) + alpha = self.rope_parameters.get("alpha", 50) + base = self.rope_parameters.get("rope_theta", 10000.0) dim = hparams["head_dim"] scaled_base = base * (alpha ** (dim / (dim - 2))) self.gguf_writer.add_rope_freq_base(scaled_base) @@ -9585,13 +9531,6 @@ class GptOssModel(TextModel): self.gguf_writer.add_sliding_window(self.hparams["sliding_window"]) self.gguf_writer.add_expert_feed_forward_length(self.hparams["intermediate_size"]) - rope_scaling = self.hparams.get("rope_scaling") or {} - rope_type = rope_scaling.get("rope_type", rope_scaling.get("type")) - assert rope_type == "yarn", f"GPT-OSS only supports yarn rope scaling, got {rope_type}" - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling.get("original_max_position_embeddings", 4096)) - @ModelBase.register("Lfm2ForCausalLM", "LFM2ForCausalLM") class LFM2Model(TextModel): @@ -9764,13 +9703,6 @@ class SmallThinkerModel(TextModel): self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX) else: self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID) - # YaRN is not enabled by default - # To enable it, please refer to this guide: https://huggingface.co/Qwen/Qwen3-30B-A3B#processing-long-texts - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) sliding_window_layout = self.hparams.get("sliding_window_layout") if sliding_window_layout: @@ -9883,6 +9815,18 @@ class MistralModel(LlamaModel): self.gguf_writer.add_architecture() self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) + def dequant_model(self): + # transform quantization config into HF format + quant_config = self.hparams.get("quantization") + if quant_config is not None: + assert quant_config["qformat_weight"] == "fp8_e4m3" + self.hparams["quantization_config"] = { + "activation_scheme": "static", + "quant_method": "fp8", + "weight_block_size": None, + } + return super().dequant_model() + @staticmethod def get_community_chat_template(vocab: MistralVocab, templates_dir: Path, is_mistral_format: bool): assert TokenizerVersion is not None and Tekkenizer is not None and SentencePieceTokenizer is not None, _mistral_import_error_msg @@ -10006,6 +9950,10 @@ class MistralMoeModel(DeepseekV2Model): MistralModel.set_mistral_config(self.gguf_writer, self.hparams) yarn_params = self.hparams["yarn"] self.gguf_writer.add_attn_temperature_length(yarn_params["original_max_position_embeddings"]) + + # [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX] + # note: for legacy reasons, this is not consistent with the other usages of self.gguf_writer.add_rope_scaling_yarn_log_mul + # ref https://github.com/ggml-org/llama.cpp/pull/17945 self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1) # mscale_all_dim * 0.1 def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None): diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index b8f694e86c..5e8456a7ea 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -143,6 +143,7 @@ models = [ {"name": "bailingmoe2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/Ling-mini-base-2.0", }, {"name": "granite-docling", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ibm-granite/granite-docling-258M", }, {"name": "minimax-m2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2", }, + {"name": "kormo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/KORMo-Team/KORMo-tokenizer", }, ] # some models are known to be broken upstream, so we will skip them as exceptions diff --git a/docs/development/HOWTO-add-model.md b/docs/development/HOWTO-add-model.md index 5989b873a6..9d1452e3f0 100644 --- a/docs/development/HOWTO-add-model.md +++ b/docs/development/HOWTO-add-model.md @@ -9,7 +9,8 @@ Adding a model requires few steps: After following these steps, you can open PR. Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially: -- [main](/tools/main/) +- [cli](/tools/cli/) +- [completion](/tools/completion/) - [imatrix](/tools/imatrix/) - [quantize](/tools/quantize/) - [server](/tools/server/) diff --git a/docs/docker.md b/docs/docker.md index 98502a0c50..b9e5015396 100644 --- a/docs/docker.md +++ b/docs/docker.md @@ -56,7 +56,7 @@ docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:light -m /model or with a server image: ```bash -docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggml-org/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 +docker run -v /path/to/models:/models -p 8080:8080 ghcr.io/ggml-org/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8080 --host 0.0.0.0 -n 512 ``` ## Docker With CUDA @@ -91,7 +91,7 @@ After building locally, Usage is similar to the non-CUDA examples, but you'll ne ```bash docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 -docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1 +docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8080 --host 0.0.0.0 -n 512 --n-gpu-layers 1 ``` ## Docker With MUSA @@ -125,5 +125,5 @@ After building locally, Usage is similar to the non-MUSA examples, but you'll ne ```bash docker run -v /path/to/models:/models local/llama.cpp:full-musa --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 docker run -v /path/to/models:/models local/llama.cpp:light-musa -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 -docker run -v /path/to/models:/models local/llama.cpp:server-musa -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1 +docker run -v /path/to/models:/models local/llama.cpp:server-musa -m /models/7B/ggml-model-q4_0.gguf --port 8080 --host 0.0.0.0 -n 512 --n-gpu-layers 1 ``` diff --git a/docs/ops.md b/docs/ops.md index ef1febccae..b395d2315c 100644 --- a/docs/ops.md +++ b/docs/ops.md @@ -16,14 +16,14 @@ Legend: |-----------|------|------|------|------|------|------|------|------|------|------|------| | ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | | ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | -| ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | +| ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | -| ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | +| ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | -| ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | +| ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ | | CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | -| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ | +| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ | ❌ | | CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ | | CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ | | CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | @@ -31,20 +31,21 @@ Legend: | CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | -| COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | +| COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | | COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | | CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| CUMSUM | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | +| CUMSUM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | +| DIAG | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ | -| DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | +| DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ | | ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | | EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | | EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| FILL | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | -| FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | +| FILL | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | +| FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ | ❌ | | FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | | GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | | GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | @@ -63,9 +64,9 @@ Legend: | IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | | L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | -| LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ | -| MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | -| MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | +| LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | +| MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | +| MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | | MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ | | NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | @@ -74,7 +75,7 @@ Legend: | OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | | OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | | OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ | -| PAD | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | ❌ | +| PAD | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ | | PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | | POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | @@ -83,7 +84,7 @@ Legend: | REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | RMS_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | -| RMS_NORM_MUL_ADD | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | +| RMS_NORM_MUL_ADD | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | @@ -97,26 +98,26 @@ Legend: | SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | | SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | | SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | -| SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | +| SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | | SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | | SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ | -| SOLVE_TRI | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | -| SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | -| SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | -| SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | +| SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | +| SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ | +| SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ | +| SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | | STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | -| SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | +| SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | SUM | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | -| SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | ❌ | +| SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ | | SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | -| SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | +| SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | -| TOP_K | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | -| TRI | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | +| TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | +| TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | | TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | -| UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | +| UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ | | XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | diff --git a/docs/ops/CPU.csv b/docs/ops/CPU.csv index 7c223e279c..fef3bbce70 100644 --- a/docs/ops/CPU.csv +++ b/docs/ops/CPU.csv @@ -4964,6 +4964,7 @@ "CPU","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","1","yes","CPU" "CPU","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","1","yes","CPU" "CPU","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","1","yes","CPU" +"CPU","CONV_TRANSPOSE_2D","ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","1","yes","CPU" "CPU","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","1","yes","CPU" "CPU","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","1","yes","CPU" "CPU","ARGMAX","type=f32,ne=[32,1,1,1]","support","1","yes","CPU" @@ -5419,17 +5420,45 @@ "CPU","CPY","type_src=f16,type_dst=f16,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","CPU" "CPU","CPY","type_src=f32,type_dst=f32,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","CPU" "CPU","CPY","type_src=bf16,type_dst=bf16,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","CPU" +"CPU","CPY","type_src=i32,type_dst=i32,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","CPU" +"CPU","CPY","type_src=i32,type_dst=i32,ne=[256,1,4,1],permute_src=[1,2,0,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","CPU" "CPU","CPY","type_src=f32,type_dst=f32,ne=[256,1,4,1],permute_src=[1,2,0,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","CPU" -"CPU","CONT","type=f32,ne=[10,10,10,1]","support","1","yes","CPU" -"CPU","CONT","type=f32,ne=[2,1,1,1]","support","1","yes","CPU" -"CPU","CONT","type=f32,ne=[2,1,3,5]","support","1","yes","CPU" -"CPU","CONT","type=f32,ne=[2,3,5,7]","support","1","yes","CPU" -"CPU","CONT","type=f16,ne=[2,1,1,1]","support","1","yes","CPU" -"CPU","CONT","type=f16,ne=[2,1,3,5]","support","1","yes","CPU" -"CPU","CONT","type=f16,ne=[2,3,5,7]","support","1","yes","CPU" -"CPU","CONT","type=bf16,ne=[2,1,1,1]","support","1","yes","CPU" -"CPU","CONT","type=bf16,ne=[2,1,3,5]","support","1","yes","CPU" -"CPU","CONT","type=bf16,ne=[2,3,5,7]","support","1","yes","CPU" +"CPU","CONT","type=f32,ne=[2,1,1,1],use_view_slice=1","support","1","yes","CPU" +"CPU","CONT","type=f32,ne=[2,1,3,5],use_view_slice=1","support","1","yes","CPU" +"CPU","CONT","type=f32,ne=[2,3,5,7],use_view_slice=1","support","1","yes","CPU" +"CPU","CONT","type=f32,ne=[1,4,4,1],use_view_slice=1","support","1","yes","CPU" +"CPU","CONT","type=f32,ne=[1,8,17,1],use_view_slice=1","support","1","yes","CPU" +"CPU","CONT","type=f32,ne=[10,10,10,1],use_view_slice=1","support","1","yes","CPU" +"CPU","CONT","type=f32,ne=[2,1,1,1],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=f32,ne=[2,1,3,5],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=f32,ne=[2,3,5,7],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=f32,ne=[1,4,4,1],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=f32,ne=[1,8,17,1],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=f32,ne=[10,10,10,1],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=i32,ne=[2,1,1,1],use_view_slice=1","support","1","yes","CPU" +"CPU","CONT","type=i32,ne=[2,1,3,5],use_view_slice=1","support","1","yes","CPU" +"CPU","CONT","type=i32,ne=[2,3,5,7],use_view_slice=1","support","1","yes","CPU" +"CPU","CONT","type=i32,ne=[1,4,4,1],use_view_slice=1","support","1","yes","CPU" +"CPU","CONT","type=i32,ne=[1,8,17,1],use_view_slice=1","support","1","yes","CPU" +"CPU","CONT","type=i32,ne=[10,10,10,1],use_view_slice=1","support","1","yes","CPU" +"CPU","CONT","type=i32,ne=[2,1,1,1],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=i32,ne=[2,1,3,5],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=i32,ne=[2,3,5,7],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=i32,ne=[1,4,4,1],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=i32,ne=[1,8,17,1],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=i32,ne=[10,10,10,1],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=f16,ne=[2,1,1,1],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=f16,ne=[2,1,3,5],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=f16,ne=[2,3,5,7],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=f16,ne=[1,4,4,1],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=f16,ne=[1,8,17,1],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=f16,ne=[10,10,10,1],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=bf16,ne=[2,1,1,1],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=bf16,ne=[2,1,3,5],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=bf16,ne=[2,3,5,7],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=bf16,ne=[1,4,4,1],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=bf16,ne=[1,8,17,1],use_view_slice=0","support","1","yes","CPU" +"CPU","CONT","type=bf16,ne=[10,10,10,1],use_view_slice=0","support","1","yes","CPU" "CPU","ADD","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","CPU" "CPU","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","CPU" "CPU","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","CPU" @@ -5655,6 +5684,7 @@ "CPU","MUL","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","CPU" "CPU","DIV","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","CPU" "CPU","ADD1","type=f32,ne=[10,5,4,3]","support","1","yes","CPU" +"CPU","ADD1","type=f32,ne=[1024,1024,1,1]","support","1","yes","CPU" 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+"CPU","TOP_K","type=f32,ne=[2048,2,1,3],k=7,ties=0","support","1","yes","CPU" +"CPU","TOP_K","type=f32,ne=[2049,2,1,3],k=7,ties=0","support","1","yes","CPU" +"CPU","TOP_K","type=f32,ne=[16,10,10,10],k=15,ties=0","support","1","yes","CPU" +"CPU","TOP_K","type=f32,ne=[60,10,10,10],k=15,ties=0","support","1","yes","CPU" +"CPU","TOP_K","type=f32,ne=[1023,2,1,3],k=15,ties=0","support","1","yes","CPU" +"CPU","TOP_K","type=f32,ne=[1024,2,1,3],k=15,ties=0","support","1","yes","CPU" +"CPU","TOP_K","type=f32,ne=[1025,2,1,3],k=15,ties=0","support","1","yes","CPU" +"CPU","TOP_K","type=f32,ne=[16384,1,1,1],k=15,ties=0","support","1","yes","CPU" +"CPU","TOP_K","type=f32,ne=[2047,2,1,3],k=15,ties=0","support","1","yes","CPU" +"CPU","TOP_K","type=f32,ne=[2048,2,1,3],k=15,ties=0","support","1","yes","CPU" +"CPU","TOP_K","type=f32,ne=[2049,2,1,3],k=15,ties=0","support","1","yes","CPU" "CPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=0","support","1","yes","CPU" "CPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=1","support","1","yes","CPU" "CPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=nearest,flags=none","support","1","yes","CPU" @@ -9435,6 +9860,10 @@ "CPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bicubic,transpose=1","support","1","yes","CPU" "CPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic,flags=none","support","1","yes","CPU" "CPU","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bicubic,flags=none","support","1","yes","CPU" +"CPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=513,transpose=0","support","1","yes","CPU" +"CPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=513,transpose=1","support","1","yes","CPU" +"CPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=none","support","1","yes","CPU" +"CPU","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear,flags=none","support","1","yes","CPU" "CPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=align_corners","support","1","yes","CPU" "CPU","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bilinear,flags=align_corners","support","1","yes","CPU" "CPU","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bilinear,flags=align_corners","support","1","yes","CPU" @@ -9463,15 +9892,30 @@ "CPU","GROUP_NORM","type=f32,ne=[64,64,320,1],num_groups=32,eps=0.000001","support","1","yes","CPU" "CPU","GROUP_NORM","type=f32,ne=[9,9,1280,1],num_groups=32,eps=0.000001","support","1","yes","CPU" "CPU","ACC","type=f32,ne_a=[256,17,1,1],ne_b=[256,16,1,1]","support","1","yes","CPU" -"CPU","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1","support","1","yes","CPU" -"CPU","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0","support","1","yes","CPU" +"CPU","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1,circular=0","support","1","yes","CPU" +"CPU","PAD","type=f32,ne_a=[33,17,2,1],pad_0=4,pad_1=3,circular=1","support","1","yes","CPU" +"CPU","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0,circular=0","support","1","yes","CPU" "CPU","PAD_REFLECT_1D","type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9","support","1","yes","CPU" "CPU","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","1","yes","CPU" "CPU","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","1","yes","CPU" "CPU","ARANGE","type=f32,start=0.000000,stop=10.000000,step=1.000000","support","1","yes","CPU" +"CPU","ARANGE","type=f32,start=0.000000,stop=1048576.000000,step=1.000000","support","1","yes","CPU" "CPU","TIMESTEP_EMBEDDING","type=f32,ne_a=[2,1,1,1],dim=320,max_period=10000","support","1","yes","CPU" "CPU","LEAKY_RELU","type=f32,ne_a=[10,5,4,3],negative_slope=0.100000","support","1","yes","CPU" "CPU","CUMSUM","type=f32,ne=[10,5,4,3]","support","1","yes","CPU" +"CPU","CUMSUM","type=f32,ne=[127,5,4,3]","support","1","yes","CPU" +"CPU","CUMSUM","type=f32,ne=[128,5,4,3]","support","1","yes","CPU" +"CPU","CUMSUM","type=f32,ne=[128,128,4,4]","support","1","yes","CPU" +"CPU","CUMSUM","type=f32,ne=[255,5,4,3]","support","1","yes","CPU" +"CPU","CUMSUM","type=f32,ne=[256,5,4,3]","support","1","yes","CPU" +"CPU","CUMSUM","type=f32,ne=[511,5,4,3]","support","1","yes","CPU" +"CPU","CUMSUM","type=f32,ne=[512,5,4,3]","support","1","yes","CPU" +"CPU","CUMSUM","type=f32,ne=[1023,5,4,3]","support","1","yes","CPU" +"CPU","CUMSUM","type=f32,ne=[1024,5,4,3]","support","1","yes","CPU" +"CPU","CUMSUM","type=f32,ne=[2047,5,4,3]","support","1","yes","CPU" +"CPU","CUMSUM","type=f32,ne=[2048,5,4,3]","support","1","yes","CPU" +"CPU","CUMSUM","type=f32,ne=[242004,1,1,1]","support","1","yes","CPU" +"CPU","CUMSUM","type=f32,ne=[375960,1,1,1]","support","1","yes","CPU" "CPU","XIELU","type=f32,ne=[10,5,4,3]","support","1","yes","CPU" "CPU","TRI","type=f32,ne=[10,10,4,3],tri_type=3","support","1","yes","CPU" "CPU","TRI","type=f32,ne=[10,10,4,3],tri_type=2","support","1","yes","CPU" @@ -9480,6 +9924,10 @@ "CPU","FILL","type=f32,ne=[10,10,4,3],c=0.000000","support","1","yes","CPU" "CPU","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","1","yes","CPU" "CPU","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","1","yes","CPU" +"CPU","FILL","type=f32,ne=[2048,512,2,2],c=3.500000","support","1","yes","CPU" +"CPU","DIAG","type=f32,ne=[10,1,4,3]","support","1","yes","CPU" +"CPU","DIAG","type=f32,ne=[79,1,19,13]","support","1","yes","CPU" +"CPU","DIAG","type=f32,ne=[256,1,8,16]","support","1","yes","CPU" "CPU","SOLVE_TRI","type=f32,ne_lhs=[10,10,4,3],ne_rhs=[3,10,4,3]","support","1","yes","CPU" "CPU","SOLVE_TRI","type=f32,ne_lhs=[11,11,1,1],ne_rhs=[5,11,1,1]","support","1","yes","CPU" "CPU","SOLVE_TRI","type=f32,ne_lhs=[17,17,2,4],ne_rhs=[9,17,2,4]","support","1","yes","CPU" @@ -9487,10 +9935,16 @@ "CPU","SOLVE_TRI","type=f32,ne_lhs=[42,42,5,2],ne_rhs=[10,42,5,2]","support","1","yes","CPU" "CPU","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[10,64,2,2]","support","1","yes","CPU" "CPU","SOLVE_TRI","type=f32,ne_lhs=[100,100,4,4],ne_rhs=[41,100,4,4]","support","1","yes","CPU" -"CPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0","support","1","yes","CPU" -"CPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0","support","1","yes","CPU" -"CPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1","support","1","yes","CPU" -"CPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1","support","1","yes","CPU" +"CPU","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[31,128,4,4]","support","1","yes","CPU" +"CPU","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[300,64,4,4]","support","1","yes","CPU" +"CPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=0","support","1","yes","CPU" +"CPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=0","support","1","yes","CPU" +"CPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=1","support","1","yes","CPU" +"CPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=1","support","1","yes","CPU" +"CPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=0","support","1","yes","CPU" +"CPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=0","support","1","yes","CPU" +"CPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=1","support","1","yes","CPU" +"CPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=1","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" diff --git a/docs/ops/CUDA.csv b/docs/ops/CUDA.csv index 56ba32273a..22c84dd143 100644 --- a/docs/ops/CUDA.csv +++ b/docs/ops/CUDA.csv @@ -4964,6 +4964,7 @@ "CUDA0","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","1","yes","CUDA" "CUDA0","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","1","yes","CUDA" "CUDA0","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","1","yes","CUDA" +"CUDA0","CONV_TRANSPOSE_2D","ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","1","yes","CUDA" "CUDA0","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","1","yes","CUDA" "CUDA0","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","1","yes","CUDA" "CUDA0","ARGMAX","type=f32,ne=[32,1,1,1]","support","1","yes","CUDA" @@ -5419,17 +5420,45 @@ "CUDA0","CPY","type_src=f16,type_dst=f16,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","CUDA" "CUDA0","CPY","type_src=f32,type_dst=f32,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","CUDA" "CUDA0","CPY","type_src=bf16,type_dst=bf16,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","CUDA" +"CUDA0","CPY","type_src=i32,type_dst=i32,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","CUDA" +"CUDA0","CPY","type_src=i32,type_dst=i32,ne=[256,1,4,1],permute_src=[1,2,0,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","CUDA" "CUDA0","CPY","type_src=f32,type_dst=f32,ne=[256,1,4,1],permute_src=[1,2,0,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","CUDA" -"CUDA0","CONT","type=f32,ne=[10,10,10,1]","support","1","yes","CUDA" -"CUDA0","CONT","type=f32,ne=[2,1,1,1]","support","1","yes","CUDA" -"CUDA0","CONT","type=f32,ne=[2,1,3,5]","support","1","yes","CUDA" -"CUDA0","CONT","type=f32,ne=[2,3,5,7]","support","1","yes","CUDA" -"CUDA0","CONT","type=f16,ne=[2,1,1,1]","support","1","yes","CUDA" -"CUDA0","CONT","type=f16,ne=[2,1,3,5]","support","1","yes","CUDA" -"CUDA0","CONT","type=f16,ne=[2,3,5,7]","support","1","yes","CUDA" -"CUDA0","CONT","type=bf16,ne=[2,1,1,1]","support","1","yes","CUDA" -"CUDA0","CONT","type=bf16,ne=[2,1,3,5]","support","1","yes","CUDA" -"CUDA0","CONT","type=bf16,ne=[2,3,5,7]","support","1","yes","CUDA" +"CUDA0","CONT","type=f32,ne=[2,1,1,1],use_view_slice=1","support","1","yes","CUDA" +"CUDA0","CONT","type=f32,ne=[2,1,3,5],use_view_slice=1","support","1","yes","CUDA" +"CUDA0","CONT","type=f32,ne=[2,3,5,7],use_view_slice=1","support","1","yes","CUDA" +"CUDA0","CONT","type=f32,ne=[1,4,4,1],use_view_slice=1","support","1","yes","CUDA" +"CUDA0","CONT","type=f32,ne=[1,8,17,1],use_view_slice=1","support","1","yes","CUDA" +"CUDA0","CONT","type=f32,ne=[10,10,10,1],use_view_slice=1","support","1","yes","CUDA" +"CUDA0","CONT","type=f32,ne=[2,1,1,1],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=f32,ne=[2,1,3,5],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=f32,ne=[2,3,5,7],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=f32,ne=[1,4,4,1],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=f32,ne=[1,8,17,1],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=f32,ne=[10,10,10,1],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=i32,ne=[2,1,1,1],use_view_slice=1","support","1","yes","CUDA" +"CUDA0","CONT","type=i32,ne=[2,1,3,5],use_view_slice=1","support","1","yes","CUDA" +"CUDA0","CONT","type=i32,ne=[2,3,5,7],use_view_slice=1","support","1","yes","CUDA" +"CUDA0","CONT","type=i32,ne=[1,4,4,1],use_view_slice=1","support","1","yes","CUDA" +"CUDA0","CONT","type=i32,ne=[1,8,17,1],use_view_slice=1","support","1","yes","CUDA" +"CUDA0","CONT","type=i32,ne=[10,10,10,1],use_view_slice=1","support","1","yes","CUDA" +"CUDA0","CONT","type=i32,ne=[2,1,1,1],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=i32,ne=[2,1,3,5],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=i32,ne=[2,3,5,7],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=i32,ne=[1,4,4,1],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=i32,ne=[1,8,17,1],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=i32,ne=[10,10,10,1],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=f16,ne=[2,1,1,1],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=f16,ne=[2,1,3,5],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=f16,ne=[2,3,5,7],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=f16,ne=[1,4,4,1],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=f16,ne=[1,8,17,1],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=f16,ne=[10,10,10,1],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=bf16,ne=[2,1,1,1],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=bf16,ne=[2,1,3,5],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=bf16,ne=[2,3,5,7],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=bf16,ne=[1,4,4,1],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=bf16,ne=[1,8,17,1],use_view_slice=0","support","1","yes","CUDA" +"CUDA0","CONT","type=bf16,ne=[10,10,10,1],use_view_slice=0","support","1","yes","CUDA" "CUDA0","ADD","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","CUDA" "CUDA0","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","CUDA" "CUDA0","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","CUDA" @@ -5655,6 +5684,7 @@ "CUDA0","MUL","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","CUDA" "CUDA0","DIV","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","CUDA" "CUDA0","ADD1","type=f32,ne=[10,5,4,3]","support","1","yes","CUDA" +"CUDA0","ADD1","type=f32,ne=[1024,1024,1,1]","support","1","yes","CUDA" "CUDA0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=0.000000,inplace=0","support","1","yes","CUDA" "CUDA0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=0","support","1","yes","CUDA" "CUDA0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=1","support","1","yes","CUDA" @@ -8644,9 +8674,13 @@ "CUDA0","CLAMP","type=f16,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","CUDA" "CUDA0","LEAKY_RELU","type=f16,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","CUDA" "CUDA0","FLOOR","type=f16,ne=[7,1,5,3]","support","1","yes","CUDA" +"CUDA0","FLOOR","type=f16,ne=[1024,1024,1,1]","support","1","yes","CUDA" "CUDA0","CEIL","type=f16,ne=[7,1,5,3]","support","1","yes","CUDA" +"CUDA0","CEIL","type=f16,ne=[1024,1024,1,1]","support","1","yes","CUDA" "CUDA0","ROUND","type=f16,ne=[7,1,5,3]","support","1","yes","CUDA" +"CUDA0","ROUND","type=f16,ne=[1024,1024,1,1]","support","1","yes","CUDA" "CUDA0","TRUNC","type=f16,ne=[7,1,5,3]","support","1","yes","CUDA" +"CUDA0","TRUNC","type=f16,ne=[1024,1024,1,1]","support","1","yes","CUDA" "CUDA0","SQR","type=f32,ne=[10,5,4,3]","support","1","yes","CUDA" "CUDA0","SQRT","type=f32,ne=[10,3,3,2]","support","1","yes","CUDA" "CUDA0","LOG","type=f32,ne=[10,5,4,3]","support","1","yes","CUDA" @@ -8666,9 +8700,13 @@ "CUDA0","CLAMP","type=f32,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","CUDA" "CUDA0","LEAKY_RELU","type=f32,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","CUDA" "CUDA0","FLOOR","type=f32,ne=[7,1,5,3]","support","1","yes","CUDA" +"CUDA0","FLOOR","type=f32,ne=[1024,1024,1,1]","support","1","yes","CUDA" "CUDA0","CEIL","type=f32,ne=[7,1,5,3]","support","1","yes","CUDA" +"CUDA0","CEIL","type=f32,ne=[1024,1024,1,1]","support","1","yes","CUDA" "CUDA0","ROUND","type=f32,ne=[7,1,5,3]","support","1","yes","CUDA" +"CUDA0","ROUND","type=f32,ne=[1024,1024,1,1]","support","1","yes","CUDA" "CUDA0","TRUNC","type=f32,ne=[7,1,5,3]","support","1","yes","CUDA" +"CUDA0","TRUNC","type=f32,ne=[1024,1024,1,1]","support","1","yes","CUDA" "CUDA0","DIAG_MASK_INF","type=f32,ne=[10,10,1,1],n_past=5","support","1","yes","CUDA" "CUDA0","DIAG_MASK_INF","type=f32,ne=[10,10,3,1],n_past=5","support","1","yes","CUDA" "CUDA0","DIAG_MASK_INF","type=f32,ne=[10,10,3,2],n_past=5","support","1","yes","CUDA" @@ -9411,18 +9449,405 @@ "CUDA0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","0","no","CUDA" "CUDA0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","CUDA" "CUDA0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","0","no","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[3,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[4,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[7,1,1,1],order=0","support","1","yes","CUDA" "CUDA0","ARGSORT","type=f32,ne=[8,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[15,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[16,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[31,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[32,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[63,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[64,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[127,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[128,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[255,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[256,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[511,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[512,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[1023,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[1024,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[2047,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[2048,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[4095,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[4096,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[8191,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[8192,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[16383,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[16384,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[32767,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[32768,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[65535,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[65536,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[131071,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[131072,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[262143,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[262144,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[524287,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[524288,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[1048575,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[1048576,1,1,1],order=0","support","1","yes","CUDA" "CUDA0","ARGSORT","type=f32,ne=[16,10,10,10],order=0","support","1","yes","CUDA" "CUDA0","ARGSORT","type=f32,ne=[60,10,10,10],order=0","support","1","yes","CUDA" -"CUDA0","ARGSORT","type=f32,ne=[1024,1,1,1],order=0","support","1","yes","CUDA" -"CUDA0","ARGSORT","type=f32,ne=[16384,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[1023,2,1,3],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[1024,2,1,3],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[1025,2,1,3],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[2047,2,1,3],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[2048,2,1,3],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[2049,2,1,3],order=0","support","1","yes","CUDA" "CUDA0","ARGSORT","type=f32,ne=[2,8,8192,1],order=0","support","1","yes","CUDA" -"CUDA0","ARGSORT","type=f32,ne=[8,1,1,1],order=1","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[3,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[4,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[7,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[8,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[15,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[16,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[31,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[32,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[63,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[64,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[127,1,1,1],order=0","support","1","yes","CUDA" +"CUDA0","ARGSORT","type=f32,ne=[128,1,1,1],order=0","support","1","yes","CUDA" 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+"CUDA0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=none","support","1","yes","CUDA" +"CUDA0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear,flags=none","support","1","yes","CUDA" "CUDA0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=align_corners","support","1","yes","CUDA" "CUDA0","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bilinear,flags=align_corners","support","1","yes","CUDA" "CUDA0","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bilinear,flags=align_corners","support","1","yes","CUDA" @@ -9463,34 +9892,59 @@ "CUDA0","GROUP_NORM","type=f32,ne=[64,64,320,1],num_groups=32,eps=0.000001","support","1","yes","CUDA" "CUDA0","GROUP_NORM","type=f32,ne=[9,9,1280,1],num_groups=32,eps=0.000001","support","1","yes","CUDA" "CUDA0","ACC","type=f32,ne_a=[256,17,1,1],ne_b=[256,16,1,1]","support","1","yes","CUDA" -"CUDA0","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1","support","1","yes","CUDA" -"CUDA0","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0","support","1","yes","CUDA" +"CUDA0","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1,circular=0","support","1","yes","CUDA" +"CUDA0","PAD","type=f32,ne_a=[33,17,2,1],pad_0=4,pad_1=3,circular=1","support","1","yes","CUDA" +"CUDA0","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0,circular=0","support","1","yes","CUDA" "CUDA0","PAD_REFLECT_1D","type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9","support","1","yes","CUDA" "CUDA0","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","1","yes","CUDA" "CUDA0","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","1","yes","CUDA" "CUDA0","ARANGE","type=f32,start=0.000000,stop=10.000000,step=1.000000","support","1","yes","CUDA" +"CUDA0","ARANGE","type=f32,start=0.000000,stop=1048576.000000,step=1.000000","support","1","yes","CUDA" "CUDA0","TIMESTEP_EMBEDDING","type=f32,ne_a=[2,1,1,1],dim=320,max_period=10000","support","1","yes","CUDA" "CUDA0","LEAKY_RELU","type=f32,ne_a=[10,5,4,3],negative_slope=0.100000","support","1","yes","CUDA" -"CUDA0","CUMSUM","type=f32,ne=[10,5,4,3]","support","0","no","CUDA" +"CUDA0","CUMSUM","type=f32,ne=[10,5,4,3]","support","1","yes","CUDA" +"CUDA0","CUMSUM","type=f32,ne=[127,5,4,3]","support","1","yes","CUDA" +"CUDA0","CUMSUM","type=f32,ne=[128,5,4,3]","support","1","yes","CUDA" +"CUDA0","CUMSUM","type=f32,ne=[128,128,4,4]","support","1","yes","CUDA" +"CUDA0","CUMSUM","type=f32,ne=[255,5,4,3]","support","1","yes","CUDA" +"CUDA0","CUMSUM","type=f32,ne=[256,5,4,3]","support","1","yes","CUDA" +"CUDA0","CUMSUM","type=f32,ne=[511,5,4,3]","support","1","yes","CUDA" +"CUDA0","CUMSUM","type=f32,ne=[512,5,4,3]","support","1","yes","CUDA" +"CUDA0","CUMSUM","type=f32,ne=[1023,5,4,3]","support","1","yes","CUDA" +"CUDA0","CUMSUM","type=f32,ne=[1024,5,4,3]","support","1","yes","CUDA" +"CUDA0","CUMSUM","type=f32,ne=[2047,5,4,3]","support","1","yes","CUDA" +"CUDA0","CUMSUM","type=f32,ne=[2048,5,4,3]","support","1","yes","CUDA" +"CUDA0","CUMSUM","type=f32,ne=[242004,1,1,1]","support","1","yes","CUDA" +"CUDA0","CUMSUM","type=f32,ne=[375960,1,1,1]","support","1","yes","CUDA" "CUDA0","XIELU","type=f32,ne=[10,5,4,3]","support","0","no","CUDA" -"CUDA0","TRI","type=f32,ne=[10,10,4,3],tri_type=3","support","0","no","CUDA" -"CUDA0","TRI","type=f32,ne=[10,10,4,3],tri_type=2","support","0","no","CUDA" -"CUDA0","TRI","type=f32,ne=[10,10,4,3],tri_type=1","support","0","no","CUDA" -"CUDA0","TRI","type=f32,ne=[10,10,4,3],tri_type=0","support","0","no","CUDA" -"CUDA0","FILL","type=f32,ne=[10,10,4,3],c=0.000000","support","0","no","CUDA" -"CUDA0","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","0","no","CUDA" -"CUDA0","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","0","no","CUDA" -"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[10,10,4,3],ne_rhs=[3,10,4,3]","support","0","no","CUDA" -"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[11,11,1,1],ne_rhs=[5,11,1,1]","support","0","no","CUDA" -"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[17,17,2,4],ne_rhs=[9,17,2,4]","support","0","no","CUDA" -"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[30,30,7,1],ne_rhs=[8,30,7,1]","support","0","no","CUDA" -"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[42,42,5,2],ne_rhs=[10,42,5,2]","support","0","no","CUDA" -"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[10,64,2,2]","support","0","no","CUDA" +"CUDA0","TRI","type=f32,ne=[10,10,4,3],tri_type=3","support","1","yes","CUDA" +"CUDA0","TRI","type=f32,ne=[10,10,4,3],tri_type=2","support","1","yes","CUDA" +"CUDA0","TRI","type=f32,ne=[10,10,4,3],tri_type=1","support","1","yes","CUDA" +"CUDA0","TRI","type=f32,ne=[10,10,4,3],tri_type=0","support","1","yes","CUDA" +"CUDA0","FILL","type=f32,ne=[10,10,4,3],c=0.000000","support","1","yes","CUDA" +"CUDA0","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","1","yes","CUDA" +"CUDA0","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","1","yes","CUDA" +"CUDA0","FILL","type=f32,ne=[2048,512,2,2],c=3.500000","support","1","yes","CUDA" +"CUDA0","DIAG","type=f32,ne=[10,1,4,3]","support","1","yes","CUDA" +"CUDA0","DIAG","type=f32,ne=[79,1,19,13]","support","1","yes","CUDA" +"CUDA0","DIAG","type=f32,ne=[256,1,8,16]","support","1","yes","CUDA" +"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[10,10,4,3],ne_rhs=[3,10,4,3]","support","1","yes","CUDA" +"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[11,11,1,1],ne_rhs=[5,11,1,1]","support","1","yes","CUDA" +"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[17,17,2,4],ne_rhs=[9,17,2,4]","support","1","yes","CUDA" +"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[30,30,7,1],ne_rhs=[8,30,7,1]","support","1","yes","CUDA" +"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[42,42,5,2],ne_rhs=[10,42,5,2]","support","1","yes","CUDA" +"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[10,64,2,2]","support","1","yes","CUDA" "CUDA0","SOLVE_TRI","type=f32,ne_lhs=[100,100,4,4],ne_rhs=[41,100,4,4]","support","0","no","CUDA" -"CUDA0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0","support","1","yes","CUDA" -"CUDA0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0","support","1","yes","CUDA" -"CUDA0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1","support","0","no","CUDA" -"CUDA0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1","support","0","no","CUDA" +"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[31,128,4,4]","support","0","no","CUDA" +"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[300,64,4,4]","support","0","no","CUDA" +"CUDA0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=0","support","1","yes","CUDA" +"CUDA0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=0","support","1","yes","CUDA" +"CUDA0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=1","support","1","yes","CUDA" +"CUDA0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=1","support","1","yes","CUDA" +"CUDA0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=0","support","0","no","CUDA" +"CUDA0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=0","support","0","no","CUDA" +"CUDA0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=1","support","0","no","CUDA" +"CUDA0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=1","support","0","no","CUDA" "CUDA0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CUDA" "CUDA0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CUDA" "CUDA0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","CUDA" diff --git a/docs/ops/OpenCL.csv b/docs/ops/OpenCL.csv index df352e1ea1..997d8a4206 100644 --- a/docs/ops/OpenCL.csv +++ b/docs/ops/OpenCL.csv @@ -27,8 +27,20 @@ "OpenCL","HARDSIGMOID","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL" "OpenCL","EXP","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL" "OpenCL","EXP","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL" +"OpenCL","EXPM1","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL" +"OpenCL","EXPM1","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL" +"OpenCL","SOFTPLUS","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL" +"OpenCL","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL" "OpenCL","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL" "OpenCL","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL" +"OpenCL","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL" +"OpenCL","FLOOR","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL" +"OpenCL","CEIL","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL" +"OpenCL","CEIL","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL" +"OpenCL","ROUND","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL" +"OpenCL","ROUND","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL" +"OpenCL","TRUNC","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL" +"OpenCL","TRUNC","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL" "OpenCL","ABS","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL" "OpenCL","ABS","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL" "OpenCL","SGN","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL" @@ -57,8 +69,20 @@ "OpenCL","HARDSIGMOID","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL" "OpenCL","EXP","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL" "OpenCL","EXP","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL" +"OpenCL","EXPM1","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL" +"OpenCL","EXPM1","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL" +"OpenCL","SOFTPLUS","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL" +"OpenCL","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL" "OpenCL","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL" "OpenCL","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL" +"OpenCL","FLOOR","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL" +"OpenCL","FLOOR","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL" +"OpenCL","CEIL","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL" +"OpenCL","CEIL","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL" +"OpenCL","ROUND","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL" +"OpenCL","ROUND","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL" +"OpenCL","TRUNC","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL" +"OpenCL","TRUNC","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL" "OpenCL","ABS","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL" "OpenCL","ABS","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL" "OpenCL","SGN","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL" @@ -87,8 +111,20 @@ "OpenCL","HARDSIGMOID","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL" "OpenCL","EXP","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL" "OpenCL","EXP","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL" +"OpenCL","EXPM1","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL" +"OpenCL","EXPM1","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL" +"OpenCL","SOFTPLUS","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL" +"OpenCL","SOFTPLUS","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL" "OpenCL","GELU_ERF","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","OpenCL" "OpenCL","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","OpenCL" +"OpenCL","FLOOR","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL" +"OpenCL","FLOOR","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL" +"OpenCL","CEIL","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL" +"OpenCL","CEIL","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL" +"OpenCL","ROUND","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL" +"OpenCL","ROUND","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL" +"OpenCL","TRUNC","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL" +"OpenCL","TRUNC","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL" "OpenCL","ABS","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL" "OpenCL","ABS","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL" "OpenCL","SGN","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL" @@ -117,8 +153,20 @@ "OpenCL","HARDSIGMOID","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL" 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+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","OpenCL" +"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL" +"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL" +"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL" +"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL" +"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","OpenCL" +"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL" +"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL" +"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL" +"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL" +"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","OpenCL" +"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL" +"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL" +"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL" +"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL" "OpenCL","CROSS_ENTROPY_LOSS","type=f32,ne=[10,5,4,3]","support","0","no","OpenCL" "OpenCL","CROSS_ENTROPY_LOSS","type=f32,ne=[30000,1,1,1]","support","0","no","OpenCL" "OpenCL","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[10,5,4,3]","support","0","no","OpenCL" "OpenCL","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[30000,1,1,1]","support","0","no","OpenCL" "OpenCL","OPT_STEP_ADAMW","type=f32,ne=[10,5,4,3]","support","0","no","OpenCL" +"OpenCL","OPT_STEP_SGD","type=f32,ne=[10,5,4,3]","support","0","no","OpenCL" diff --git a/docs/ops/SYCL.csv b/docs/ops/SYCL.csv index 85a45d6ae0..91b442bde8 100644 --- a/docs/ops/SYCL.csv +++ b/docs/ops/SYCL.csv @@ -33,14 +33,14 @@ "SYCL0","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" "SYCL0","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" -"SYCL0","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" -"SYCL0","FLOOR","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" -"SYCL0","CEIL","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" -"SYCL0","CEIL","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" -"SYCL0","ROUND","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" -"SYCL0","ROUND","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" -"SYCL0","TRUNC","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" -"SYCL0","TRUNC","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" +"SYCL0","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","FLOOR","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","CEIL","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","CEIL","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","ROUND","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","ROUND","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","TRUNC","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","TRUNC","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" "SYCL0","ABS","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" "SYCL0","ABS","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" "SYCL0","SGN","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" @@ -287,14 +287,14 @@ "SYCL0","GEGLU_QUICK","type=f32,ne_a=[5,7,11,13],v=1,swapped=1","support","1","yes","SYCL" "SYCL0","GEGLU_QUICK","type=f32,ne_a=[128,2,2,2],v=1,split","support","1","yes","SYCL" "SYCL0","GEGLU_QUICK","type=f32,ne_a=[5,7,11,13],v=1,split","support","1","yes","SYCL" -"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=0.500000,limit=2.000000","support","0","no","SYCL" -"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=0.500000,limit=7.000000","support","0","no","SYCL" -"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=1.702000,limit=2.000000","support","0","no","SYCL" -"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=1.702000,limit=7.000000","support","0","no","SYCL" -"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=0.500000,limit=2.000000","support","0","no","SYCL" -"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=0.500000,limit=7.000000","support","0","no","SYCL" -"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=1.702000,limit=2.000000","support","0","no","SYCL" -"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=1.702000,limit=7.000000","support","0","no","SYCL" +"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=0.500000,limit=2.000000","support","1","yes","SYCL" +"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=0.500000,limit=7.000000","support","1","yes","SYCL" +"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=1.702000,limit=2.000000","support","1","yes","SYCL" +"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=1.702000,limit=7.000000","support","1","yes","SYCL" +"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=0.500000,limit=2.000000","support","1","yes","SYCL" +"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=0.500000,limit=7.000000","support","1","yes","SYCL" +"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=1.702000,limit=2.000000","support","1","yes","SYCL" +"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=1.702000,limit=7.000000","support","1","yes","SYCL" "SYCL0","GET_ROWS","type=f32,n=76800,m=5,r=4,be1=1,be2=2,v=0","support","1","yes","SYCL" "SYCL0","GET_ROWS","type=f32,n=256,m=80000,r=70000,be1=2,be2=1,v=0","support","1","yes","SYCL" "SYCL0","GET_ROWS","type=f32,n=256,m=5,r=4,be1=700,be2=100,v=0","support","1","yes","SYCL" @@ -4964,6 +4964,7 @@ "SYCL0","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","1","yes","SYCL" "SYCL0","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","SYCL" "SYCL0","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","SYCL" +"SYCL0","CONV_TRANSPOSE_2D","ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","0","no","SYCL" "SYCL0","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","1","yes","SYCL" "SYCL0","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","1","yes","SYCL" "SYCL0","ARGMAX","type=f32,ne=[32,1,1,1]","support","1","yes","SYCL" @@ -5419,17 +5420,45 @@ "SYCL0","CPY","type_src=f16,type_dst=f16,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","SYCL" "SYCL0","CPY","type_src=f32,type_dst=f32,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","SYCL" "SYCL0","CPY","type_src=bf16,type_dst=bf16,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","0","no","SYCL" +"SYCL0","CPY","type_src=i32,type_dst=i32,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","0","no","SYCL" +"SYCL0","CPY","type_src=i32,type_dst=i32,ne=[256,1,4,1],permute_src=[1,2,0,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL" "SYCL0","CPY","type_src=f32,type_dst=f32,ne=[256,1,4,1],permute_src=[1,2,0,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL" -"SYCL0","CONT","type=f32,ne=[10,10,10,1]","support","1","yes","SYCL" -"SYCL0","CONT","type=f32,ne=[2,1,1,1]","support","1","yes","SYCL" -"SYCL0","CONT","type=f32,ne=[2,1,3,5]","support","1","yes","SYCL" -"SYCL0","CONT","type=f32,ne=[2,3,5,7]","support","1","yes","SYCL" -"SYCL0","CONT","type=f16,ne=[2,1,1,1]","support","1","yes","SYCL" -"SYCL0","CONT","type=f16,ne=[2,1,3,5]","support","1","yes","SYCL" -"SYCL0","CONT","type=f16,ne=[2,3,5,7]","support","1","yes","SYCL" -"SYCL0","CONT","type=bf16,ne=[2,1,1,1]","support","0","no","SYCL" -"SYCL0","CONT","type=bf16,ne=[2,1,3,5]","support","0","no","SYCL" -"SYCL0","CONT","type=bf16,ne=[2,3,5,7]","support","0","no","SYCL" +"SYCL0","CONT","type=f32,ne=[2,1,1,1],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[2,1,3,5],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[2,3,5,7],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[1,4,4,1],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[1,8,17,1],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[10,10,10,1],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[2,1,1,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[2,1,3,5],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[2,3,5,7],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[1,4,4,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[1,8,17,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[10,10,10,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[2,1,1,1],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[2,1,3,5],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[2,3,5,7],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[1,4,4,1],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[1,8,17,1],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[10,10,10,1],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[2,1,1,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[2,1,3,5],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[2,3,5,7],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[1,4,4,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[1,8,17,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[10,10,10,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f16,ne=[2,1,1,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f16,ne=[2,1,3,5],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f16,ne=[2,3,5,7],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f16,ne=[1,4,4,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f16,ne=[1,8,17,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f16,ne=[10,10,10,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=bf16,ne=[2,1,1,1],use_view_slice=0","support","0","no","SYCL" +"SYCL0","CONT","type=bf16,ne=[2,1,3,5],use_view_slice=0","support","0","no","SYCL" +"SYCL0","CONT","type=bf16,ne=[2,3,5,7],use_view_slice=0","support","0","no","SYCL" +"SYCL0","CONT","type=bf16,ne=[1,4,4,1],use_view_slice=0","support","0","no","SYCL" +"SYCL0","CONT","type=bf16,ne=[1,8,17,1],use_view_slice=0","support","0","no","SYCL" +"SYCL0","CONT","type=bf16,ne=[10,10,10,1],use_view_slice=0","support","0","no","SYCL" "SYCL0","ADD","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" @@ -5655,6 +5684,7 @@ "SYCL0","MUL","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","DIV","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","ADD1","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" +"SYCL0","ADD1","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL" "SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=0.000000,inplace=0","support","1","yes","SYCL" "SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=0","support","1","yes","SYCL" "SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=1","support","1","yes","SYCL" @@ -5791,15 +5821,15 @@ "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -5944,15 +5974,15 @@ "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" @@ -5971,15 +6001,15 @@ "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6002,15 +6032,15 @@ "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" @@ -6029,15 +6059,15 @@ "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6176,15 +6206,15 @@ "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6207,15 +6237,15 @@ "SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6238,15 +6268,15 @@ "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" 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"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6269,15 +6299,15 @@ "SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" 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+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6300,15 +6330,15 @@ "SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" 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"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6331,15 +6361,15 @@ "SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" 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+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6362,15 +6392,15 @@ "SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" 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"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" 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"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" 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-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" @@ -6489,12 +6519,12 @@ "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6520,12 +6550,12 @@ "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6807,145 +6837,145 @@ "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" 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"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=16,n_used=16,b=0,m=32,n=1024,k=16","support","1","yes","SYCL" "SYCL0","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=2,n_used=2,b=0,m=32,n=8192,k=64","support","1","yes","SYCL" @@ -6954,7 +6984,7 @@ "SYCL0","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=2,n_used=2,b=1,m=32,n=8192,k=64","support","1","yes","SYCL" "SYCL0","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=16,n_used=16,b=1,m=50,n=200,k=64","support","1","yes","SYCL" "SYCL0","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=1,n_used=1,b=0,m=8,n=16,k=1","support","1","yes","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=32,n_used=2,b=0,m=2880,n=32,k=2880","support","0","no","SYCL" +"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=32,n_used=2,b=0,m=2880,n=32,k=2880","support","1","yes","SYCL" "SYCL0","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=1,k=256","support","1","yes","SYCL" "SYCL0","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=4,k=256","support","1","yes","SYCL" "SYCL0","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=5,k=256","support","1","yes","SYCL" @@ -7387,78 +7417,78 @@ "SYCL0","MUL_MAT_ID","type_a=q4_K,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=17,k=256","support","1","yes","SYCL" "SYCL0","MUL_MAT_ID","type_a=q4_K,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=32,k=256","support","1","yes","SYCL" "SYCL0","MUL_MAT_ID","type_a=q4_K,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=129,k=256","support","1","yes","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=1,k=256","support","0","no","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=4,k=256","support","0","no","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=5,k=256","support","0","no","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=17,k=256","support","0","no","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=32,k=256","support","0","no","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=129,k=256","support","0","no","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=1,k=256","support","0","no","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=4,k=256","support","0","no","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=5,k=256","support","0","no","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=17,k=256","support","0","no","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=32,k=256","support","0","no","SYCL" 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"SYCL0","OUT_PROD","type_a=iq2_xxs,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL" "SYCL0","OUT_PROD","type_a=iq2_xxs,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL" "SYCL0","OUT_PROD","type_a=iq2_xxs,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=1","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=32","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=129","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=1,n_token=1","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=1,n_token=32","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=1,n_token=129","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=2,n_token=1","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=2,n_token=32","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=2,n_token=129","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=2,n_token=1","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=2,n_token=32","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=2,n_token=129","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=4,n_token=1","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=4,n_token=32","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=4,n_token=129","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=4,n_token=1","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=4,n_token=32","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=4,n_token=129","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=1,n_token=1","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=1,n_token=32","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=1,n_token=129","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=1,n_token=1","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=1,n_token=32","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=1,n_token=129","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=2,n_token=1","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=2,n_token=32","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=2,n_token=129","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=2,n_token=1","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=2,n_token=32","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=2,n_token=129","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=1","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=32","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=129","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=1","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=32","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=129","support","0","no","SYCL" -"SYCL0","SQR","type=f16,ne=[10,5,4,3]","support","0","no","SYCL" -"SYCL0","SQRT","type=f16,ne=[10,3,3,2]","support","0","no","SYCL" -"SYCL0","LOG","type=f16,ne=[10,5,4,3]","support","0","no","SYCL" -"SYCL0","SIN","type=f16,ne=[10,2,2,2]","support","0","no","SYCL" -"SYCL0","COS","type=f16,ne=[10,2,2,2]","support","0","no","SYCL" -"SYCL0","CLAMP","type=f16,ne=[10,5,4,3],min=-0.500000,max=0.500000","support","0","no","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=1","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=32","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=129","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=1,n_token=1","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=1,n_token=32","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=1,n_token=129","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=2,n_token=1","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=2,n_token=32","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=2,n_token=129","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=2,n_token=1","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=2,n_token=32","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=2,n_token=129","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=4,n_token=1","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=4,n_token=32","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=4,n_token=129","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=4,n_token=1","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=4,n_token=32","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=4,n_token=129","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=1,n_token=1","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=1,n_token=32","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=1,n_token=129","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=1,n_token=1","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=1,n_token=32","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=1,n_token=129","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=2,n_token=1","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=2,n_token=32","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=2,n_token=129","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=2,n_token=1","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=2,n_token=32","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=2,n_token=129","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=1","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=32","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=129","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=1","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=32","support","1","yes","SYCL" +"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=129","support","1","yes","SYCL" +"SYCL0","SQR","type=f16,ne=[10,5,4,3]","support","1","yes","SYCL" +"SYCL0","SQRT","type=f16,ne=[10,3,3,2]","support","1","yes","SYCL" +"SYCL0","LOG","type=f16,ne=[10,5,4,3]","support","1","yes","SYCL" +"SYCL0","SIN","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL" +"SYCL0","COS","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL" +"SYCL0","CLAMP","type=f16,ne=[10,5,4,3],min=-0.500000,max=0.500000","support","1","yes","SYCL" "SYCL0","LEAKY_RELU","type=f16,ne_a=[10,5,4,3],negative_slope=0.100000","support","1","yes","SYCL" -"SYCL0","FLOOR","type=f16,ne=[10,2,2,2]","support","0","no","SYCL" -"SYCL0","CEIL","type=f16,ne=[10,2,2,2]","support","0","no","SYCL" -"SYCL0","ROUND","type=f16,ne=[10,2,2,2]","support","0","no","SYCL" -"SYCL0","TRUNC","type=f16,ne=[10,2,2,2]","support","0","no","SYCL" -"SYCL0","SQR","type=f16,ne=[7,1,5,3]","support","0","no","SYCL" -"SYCL0","SQRT","type=f16,ne=[7,1,5,3]","support","0","no","SYCL" -"SYCL0","LOG","type=f16,ne=[7,1,5,3]","support","0","no","SYCL" -"SYCL0","SIN","type=f16,ne=[7,1,5,3]","support","0","no","SYCL" -"SYCL0","COS","type=f16,ne=[7,1,5,3]","support","0","no","SYCL" -"SYCL0","CLAMP","type=f16,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","0","no","SYCL" +"SYCL0","FLOOR","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL" +"SYCL0","CEIL","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL" +"SYCL0","ROUND","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL" +"SYCL0","TRUNC","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL" +"SYCL0","SQR","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","SQRT","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","LOG","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","SIN","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","COS","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","CLAMP","type=f16,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","SYCL" "SYCL0","LEAKY_RELU","type=f16,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","SYCL" -"SYCL0","FLOOR","type=f16,ne=[7,1,5,3]","support","0","no","SYCL" -"SYCL0","CEIL","type=f16,ne=[7,1,5,3]","support","0","no","SYCL" -"SYCL0","ROUND","type=f16,ne=[7,1,5,3]","support","0","no","SYCL" -"SYCL0","TRUNC","type=f16,ne=[7,1,5,3]","support","0","no","SYCL" +"SYCL0","FLOOR","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","FLOOR","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL" +"SYCL0","CEIL","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","CEIL","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL" +"SYCL0","ROUND","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","ROUND","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL" +"SYCL0","TRUNC","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","TRUNC","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL" "SYCL0","SQR","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" "SYCL0","SQRT","type=f32,ne=[10,3,3,2]","support","1","yes","SYCL" "SYCL0","LOG","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" @@ -8666,9 +8700,13 @@ "SYCL0","CLAMP","type=f32,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","SYCL" "SYCL0","LEAKY_RELU","type=f32,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","SYCL" "SYCL0","FLOOR","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","FLOOR","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL" "SYCL0","CEIL","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","CEIL","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL" "SYCL0","ROUND","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","ROUND","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL" "SYCL0","TRUNC","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","TRUNC","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL" "SYCL0","DIAG_MASK_INF","type=f32,ne=[10,10,1,1],n_past=5","support","1","yes","SYCL" "SYCL0","DIAG_MASK_INF","type=f32,ne=[10,10,3,1],n_past=5","support","1","yes","SYCL" "SYCL0","DIAG_MASK_INF","type=f32,ne=[10,10,3,2],n_past=5","support","1","yes","SYCL" @@ -9411,28 +9449,405 @@ "SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","1","yes","SYCL" "SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","SYCL" "SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","SYCL" +"SYCL0","ARGSORT","type=f32,ne=[3,1,1,1],order=0","support","1","yes","SYCL" +"SYCL0","ARGSORT","type=f32,ne=[4,1,1,1],order=0","support","1","yes","SYCL" +"SYCL0","ARGSORT","type=f32,ne=[7,1,1,1],order=0","support","1","yes","SYCL" "SYCL0","ARGSORT","type=f32,ne=[8,1,1,1],order=0","support","1","yes","SYCL" +"SYCL0","ARGSORT","type=f32,ne=[15,1,1,1],order=0","support","1","yes","SYCL" +"SYCL0","ARGSORT","type=f32,ne=[16,1,1,1],order=0","support","1","yes","SYCL" +"SYCL0","ARGSORT","type=f32,ne=[31,1,1,1],order=0","support","1","yes","SYCL" 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+"SYCL0","TOP_K","type=f32,ne=[524288,1,1,1],k=100,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[524299,1,2,1],k=100,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[524288,1,1,1],k=500,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[524299,1,2,1],k=500,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[524288,1,1,1],k=1023,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[524299,1,2,1],k=1023,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[524288,1,1,1],k=9999,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[524299,1,2,1],k=9999,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16,10,10,10],k=1,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[60,10,10,10],k=1,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1023,2,1,3],k=1,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1024,2,1,3],k=1,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1025,2,1,3],k=1,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=1,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2047,2,1,3],k=1,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2048,2,1,3],k=1,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2049,2,1,3],k=1,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16,10,10,10],k=2,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[60,10,10,10],k=2,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1023,2,1,3],k=2,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1024,2,1,3],k=2,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1025,2,1,3],k=2,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=2,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2047,2,1,3],k=2,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2048,2,1,3],k=2,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2049,2,1,3],k=2,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16,10,10,10],k=3,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[60,10,10,10],k=3,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1023,2,1,3],k=3,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1024,2,1,3],k=3,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1025,2,1,3],k=3,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=3,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2047,2,1,3],k=3,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2048,2,1,3],k=3,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2049,2,1,3],k=3,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16,10,10,10],k=7,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[60,10,10,10],k=7,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1023,2,1,3],k=7,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1024,2,1,3],k=7,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1025,2,1,3],k=7,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=7,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2047,2,1,3],k=7,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2048,2,1,3],k=7,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2049,2,1,3],k=7,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16,10,10,10],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[60,10,10,10],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1023,2,1,3],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1024,2,1,3],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1025,2,1,3],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2047,2,1,3],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2048,2,1,3],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2049,2,1,3],k=15,ties=0","support","0","no","SYCL" "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=0","support","1","yes","SYCL" "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=1","support","1","yes","SYCL" "SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=nearest,flags=none","support","1","yes","SYCL" @@ -9445,6 +9860,10 @@ "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bicubic,transpose=1","support","0","no","SYCL" "SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic,flags=none","support","0","no","SYCL" "SYCL0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bicubic,flags=none","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=513,transpose=0","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=513,transpose=1","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=none","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear,flags=none","support","0","no","SYCL" "SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=align_corners","support","0","no","SYCL" "SYCL0","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bilinear,flags=align_corners","support","0","no","SYCL" "SYCL0","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bilinear,flags=align_corners","support","0","no","SYCL" @@ -9479,9 +9898,23 @@ "SYCL0","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","1","yes","SYCL" "SYCL0","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","1","yes","SYCL" "SYCL0","ARANGE","type=f32,start=0.000000,stop=10.000000,step=1.000000","support","1","yes","SYCL" +"SYCL0","ARANGE","type=f32,start=0.000000,stop=1048576.000000,step=1.000000","support","1","yes","SYCL" "SYCL0","TIMESTEP_EMBEDDING","type=f32,ne_a=[2,1,1,1],dim=320,max_period=10000","support","1","yes","SYCL" "SYCL0","LEAKY_RELU","type=f32,ne_a=[10,5,4,3],negative_slope=0.100000","support","1","yes","SYCL" "SYCL0","CUMSUM","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[127,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[128,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[128,128,4,4]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[255,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[256,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[511,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[512,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[1023,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[1024,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[2047,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[2048,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[242004,1,1,1]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[375960,1,1,1]","support","0","no","SYCL" "SYCL0","XIELU","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" "SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=3","support","0","no","SYCL" "SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=2","support","0","no","SYCL" @@ -9490,6 +9923,7 @@ "SYCL0","FILL","type=f32,ne=[10,10,4,3],c=0.000000","support","0","no","SYCL" "SYCL0","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","0","no","SYCL" "SYCL0","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","0","no","SYCL" +"SYCL0","FILL","type=f32,ne=[2048,512,2,2],c=3.500000","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[10,10,4,3],ne_rhs=[3,10,4,3]","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[11,11,1,1],ne_rhs=[5,11,1,1]","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[17,17,2,4],ne_rhs=[9,17,2,4]","support","0","no","SYCL" @@ -9497,6 +9931,8 @@ "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[42,42,5,2],ne_rhs=[10,42,5,2]","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[10,64,2,2]","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[100,100,4,4],ne_rhs=[41,100,4,4]","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[31,128,4,4]","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[300,64,4,4]","support","0","no","SYCL" "SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0","support","1","yes","SYCL" "SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0","support","1","yes","SYCL" "SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1","support","0","no","SYCL" diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index 1a5de5928a..36a12d299f 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -2,6 +2,7 @@ #include "common.h" #include "log.h" #include "llama.h" +#include "sampling.h" #include #include @@ -64,17 +65,23 @@ int main(int argc, char ** argv) { ctx_params.n_ctx = n_kv_req; ctx_params.n_batch = std::max(n_predict, n_parallel); - llama_context * ctx = llama_init_from_model(model, ctx_params); - auto sparams = llama_sampler_chain_default_params(); sparams.no_perf = false; - llama_sampler * smpl = llama_sampler_chain_init(sparams); + std::vector samplers; - llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k)); - llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sampling.top_p, params.sampling.min_keep)); - llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp)); - llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed)); + for (int32_t i = 0; i < n_parallel; ++i) { + llama_sampler * smpl = llama_sampler_chain_init(sparams); + + llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k)); + llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sampling.top_p, params.sampling.min_keep)); + llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp)); + llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed)); + + samplers.push_back(smpl); + } + + llama_context * ctx = llama_init_from_model(model, ctx_params); if (ctx == NULL) { LOG_ERR("%s: error: failed to create the llama_context\n" , __func__); @@ -173,7 +180,7 @@ int main(int argc, char ** argv) { continue; } - const llama_token new_token_id = llama_sampler_sample(smpl, ctx, i_batch[i]); + const llama_token new_token_id = llama_sampler_sample(samplers[i], ctx, i_batch[i]); // is it an end of generation? -> mark the stream as finished if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_predict) { @@ -229,14 +236,17 @@ int main(int argc, char ** argv) { __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f)); LOG("\n"); - llama_perf_sampler_print(smpl); + llama_perf_sampler_print(samplers[0]); llama_perf_context_print(ctx); fprintf(stderr, "\n"); llama_batch_free(batch); - llama_sampler_free(smpl); + for (auto & sampler_config : samplers) { + llama_sampler_free(sampler_config); + } + llama_free(ctx); llama_model_free(model); diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index fe91b308cd..81111e81b2 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -131,10 +131,10 @@ int main(int argc, char ** argv) { llama_numa_init(params.numa); // load the model - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); if (model == NULL) { LOG_ERR("%s: unable to load model\n", __func__); diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp index 80c693ce61..408338f1af 100644 --- a/examples/eval-callback/eval-callback.cpp +++ b/examples/eval-callback/eval-callback.cpp @@ -202,10 +202,10 @@ int main(int argc, char ** argv) { params.warmup = false; // init - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); if (model == nullptr || ctx == nullptr) { LOG_ERR("%s : failed to init\n", __func__); diff --git a/examples/gen-docs/gen-docs.cpp b/examples/gen-docs/gen-docs.cpp index 77c59a836e..e9f7bf9313 100644 --- a/examples/gen-docs/gen-docs.cpp +++ b/examples/gen-docs/gen-docs.cpp @@ -14,12 +14,13 @@ static void write_table_header(std::ofstream & file) { static void write_table_entry(std::ofstream & file, const common_arg & opt) { file << "| `"; // args - for (const auto & arg : opt.args) { - if (arg == opt.args.front()) { + auto all_args = opt.get_args(); + for (const auto & arg : all_args) { + if (arg == all_args.front()) { file << arg; - if (opt.args.size() > 1) file << ", "; + if (all_args.size() > 1) file << ", "; } else { - file << arg << (arg != opt.args.back() ? ", " : ""); + file << arg << (arg != all_args.back() ? ", " : ""); } } // value hint @@ -76,7 +77,7 @@ static void export_md(std::string fname, llama_example ex) { } int main(int, char **) { - export_md("autogen-main.md", LLAMA_EXAMPLE_MAIN); + export_md("autogen-main.md", LLAMA_EXAMPLE_COMPLETION); export_md("autogen-server.md", LLAMA_EXAMPLE_SERVER); return 0; diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp index 1e26d8221b..f54cfdd77f 100644 --- a/examples/lookahead/lookahead.cpp +++ b/examples/lookahead/lookahead.cpp @@ -55,10 +55,10 @@ int main(int argc, char ** argv) { llama_numa_init(params.numa); // load the target model - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); auto * mem = llama_get_memory(ctx); diff --git a/examples/lookup/lookup-create.cpp b/examples/lookup/lookup-create.cpp index 3da45ed9e0..bb94a8fe06 100644 --- a/examples/lookup/lookup-create.cpp +++ b/examples/lookup/lookup-create.cpp @@ -18,16 +18,16 @@ int main(int argc, char ** argv){ llama_numa_init(params.numa); // load the model - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model_ptr & model = llama_init.model; - llama_context_ptr & ctx = llama_init.context; + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); GGML_ASSERT(model != nullptr); // tokenize the prompt std::vector inp; - inp = common_tokenize(ctx.get(), params.prompt, true, true); + inp = common_tokenize(ctx, params.prompt, true, true); fprintf(stderr, "%s: tokenization done\n", __func__); common_ngram_cache ngram_cache; diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp index fcb289abe0..135f6fcab9 100644 --- a/examples/lookup/lookup-stats.cpp +++ b/examples/lookup/lookup-stats.cpp @@ -28,13 +28,13 @@ int main(int argc, char ** argv){ llama_numa_init(params.numa); // load the model - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_context_ptr & ctx = llama_init.context; + llama_context * ctx = llama_init->context(); // tokenize the prompt std::vector inp; - inp = common_tokenize(ctx.get(), params.prompt, true, true); + inp = common_tokenize(ctx, params.prompt, true, true); common_ngram_cache ngram_cache_context; common_ngram_cache ngram_cache_dynamic; @@ -65,7 +65,7 @@ int main(int argc, char ** argv){ } const int n_input = inp.size(); - const int n_ctx = llama_n_ctx(ctx.get()); + const int n_ctx = llama_n_ctx(ctx); int n_drafted = 0; int n_accept = 0; diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index 2bfa26b55f..27f159940a 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -29,10 +29,10 @@ int main(int argc, char ** argv){ llama_numa_init(params.numa); // load the model - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); const llama_vocab * vocab = llama_model_get_vocab(model); diff --git a/examples/model-conversion/scripts/causal/compare-logits.py b/examples/model-conversion/scripts/causal/compare-logits.py index afa0d5b263..894302c69e 100755 --- a/examples/model-conversion/scripts/causal/compare-logits.py +++ b/examples/model-conversion/scripts/causal/compare-logits.py @@ -1,10 +1,13 @@ #!/usr/bin/env python3 -import numpy as np import sys -import os +import numpy as np from pathlib import Path +# Add utils directory to path for direct script execution +sys.path.insert(0, str(Path(__file__).parent.parent / "utils")) +from common import get_model_name_from_env_path # type: ignore[import-not-found] + def quick_logits_check(pytorch_file, llamacpp_file): """Lightweight sanity check before NMSE""" @@ -32,27 +35,16 @@ def quick_logits_check(pytorch_file, llamacpp_file): print(f"Top 10 llama.cpp logits: {llamacpp_logits[llamacpp_top10]}") print(f"Max absolute difference: {max_diff:.4f}") - if max_diff > 1.0: - print(f"❌ NOK: Large differences detected - max diff: {max_diff:.4f}") - return False - return True def main(): - model_path = os.getenv('MODEL_PATH') - if not model_path: - print("Error: MODEL_PATH environment variable not set") - sys.exit(1) - - if not os.path.exists(model_path): - print(f"Error: Model file not found: {model_path}") - sys.exit(1) - - model_name = os.path.basename(model_path) + model_name = get_model_name_from_env_path('MODEL_PATH') data_dir = Path("data") - pytorch_file = data_dir / f"pytorch-{model_name}.bin" - llamacpp_file = data_dir / f"llamacpp-{model_name}.bin" + + llamacpp_model_name = get_model_name_from_env_path('CONVERTED_MODEL') + print(f"Using converted model: {llamacpp_model_name}") + llamacpp_file = data_dir / f"llamacpp-{llamacpp_model_name}.bin" if not pytorch_file.exists(): print(f"Error: PyTorch logits file not found: {pytorch_file}") diff --git a/examples/model-conversion/scripts/causal/run-org-model.py b/examples/model-conversion/scripts/causal/run-org-model.py index 7d2b80057c..da1132c003 100755 --- a/examples/model-conversion/scripts/causal/run-org-model.py +++ b/examples/model-conversion/scripts/causal/run-org-model.py @@ -200,7 +200,7 @@ with torch.no_grad(): logits = outputs.logits # Extract logits for the last token (next token prediction) - last_logits = logits[0, -1, :].cpu().numpy() + last_logits = logits[0, -1, :].float().cpu().numpy() print(f"Logits shape: {logits.shape}") print(f"Last token logits shape: {last_logits.shape}") diff --git a/examples/model-conversion/scripts/utils/__init__.py b/examples/model-conversion/scripts/utils/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/model-conversion/scripts/utils/check-nmse.py b/examples/model-conversion/scripts/utils/check-nmse.py index 939e3153cc..83f63f9ff3 100755 --- a/examples/model-conversion/scripts/utils/check-nmse.py +++ b/examples/model-conversion/scripts/utils/check-nmse.py @@ -5,6 +5,7 @@ import sys import os import argparse from pathlib import Path +from common import get_model_name_from_env_path # type: ignore[import-not-found] def calculate_nmse(reference, test): mse = np.mean((test - reference) ** 2) @@ -67,11 +68,13 @@ def main(): parser.add_argument('-m', '--model-path', required=True, help='Path to the model directory') args = parser.parse_args() - model_name = os.path.basename(args.model_path) + model_name = get_model_name_from_env_path('MODEL_PATH') data_dir = Path("data") pytorch_file = data_dir / f"pytorch-{model_name}.bin" - llamacpp_file = data_dir / f"llamacpp-{model_name}.bin" + + llamacpp_model_name = get_model_name_from_env_path('CONVERTED_MODEL') + llamacpp_file = data_dir / f"llamacpp-{llamacpp_model_name}.bin" print(f"Model name: {model_name}") print(f"PyTorch logits file: {pytorch_file}") diff --git a/examples/model-conversion/scripts/utils/common.py b/examples/model-conversion/scripts/utils/common.py new file mode 100644 index 0000000000..945f9a1a1d --- /dev/null +++ b/examples/model-conversion/scripts/utils/common.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python3 + +import os +import sys + +def get_model_name_from_env_path(env_path_name): + model_path = os.getenv(env_path_name) + if not model_path: + print(f"Error: {env_path_name} environment variable not set") + sys.exit(1) + + if not os.path.exists(model_path): + print(f"Error: Model file not found: {model_path}") + sys.exit(1) + + name = os.path.basename(os.path.normpath(model_path)) + if name.endswith(".gguf"): + name = name[:-5] + + return name diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index e48f48fc32..c92173ae29 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -192,10 +192,10 @@ int main(int argc, char ** argv) { llama_numa_init(params.numa); // load the target model - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); auto * mem = llama_get_memory(ctx); diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp index 042e12c2bf..2c2143ad10 100644 --- a/examples/retrieval/retrieval.cpp +++ b/examples/retrieval/retrieval.cpp @@ -149,10 +149,10 @@ int main(int argc, char ** argv) { llama_numa_init(params.numa); // load the model - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); if (model == NULL) { LOG_ERR("%s: unable to load model\n", __func__); diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index 4cd3071f76..39d4464663 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -34,10 +34,10 @@ int main(int argc, char ** argv) { std::string result2; // init - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); if (model == nullptr || ctx == nullptr) { fprintf(stderr, "%s : failed to init\n", __func__); diff --git a/examples/speculative-simple/speculative-simple.cpp b/examples/speculative-simple/speculative-simple.cpp index a8e53f28eb..8141052a22 100644 --- a/examples/speculative-simple/speculative-simple.cpp +++ b/examples/speculative-simple/speculative-simple.cpp @@ -40,10 +40,10 @@ int main(int argc, char ** argv) { llama_context * ctx_dft = NULL; // load the target model - common_init_result llama_init_tgt = common_init_from_params(params); + auto llama_init_tgt = common_init_from_params(params); - model_tgt = llama_init_tgt.model.get(); - ctx_tgt = llama_init_tgt.context.get(); + model_tgt = llama_init_tgt->model(); + ctx_tgt = llama_init_tgt->context(); const llama_vocab * vocab = llama_model_get_vocab(model_tgt); @@ -61,10 +61,10 @@ int main(int argc, char ** argv) { params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads; params.tensor_buft_overrides = params.speculative.tensor_buft_overrides; - common_init_result llama_init_dft = common_init_from_params(params); + auto llama_init_dft = common_init_from_params(params); - //model_dft = llama_init_dft.model.get(); - ctx_dft = llama_init_dft.context.get(); + //model_dft = llama_init_dft->model(); + ctx_dft = llama_init_dft->context(); if (!common_speculative_are_compatible(ctx_tgt, ctx_dft)) { LOG_INF("the draft model '%s' is not compatible with the target model '%s'. tokens will be translated between the draft and target models.\n", params.speculative.model.path.c_str(), params.model.path.c_str()); @@ -255,6 +255,8 @@ int main(int argc, char ** argv) { LOG_INF("target:\n\n"); common_perf_print(ctx_tgt, smpl); + llama_batch_free(batch_tgt); + common_sampler_free(smpl); common_speculative_free(spec); diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 5f5ac5eb64..2fb7f6374e 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -71,10 +71,10 @@ int main(int argc, char ** argv) { llama_context * ctx_dft = NULL; // load the target model - common_init_result llama_init_tgt = common_init_from_params(params); + auto llama_init_tgt = common_init_from_params(params); - model_tgt = llama_init_tgt.model.get(); - ctx_tgt = llama_init_tgt.context.get(); + model_tgt = llama_init_tgt->model(); + ctx_tgt = llama_init_tgt->context(); // load the draft model params.devices = params.speculative.devices; @@ -87,10 +87,10 @@ int main(int argc, char ** argv) { params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads; params.tensor_buft_overrides = params.speculative.tensor_buft_overrides; - common_init_result llama_init_dft = common_init_from_params(params); + auto llama_init_dft = common_init_from_params(params); - model_dft = llama_init_dft.model.get(); - ctx_dft = llama_init_dft.context.get(); + model_dft = llama_init_dft->model(); + ctx_dft = llama_init_dft->context(); const llama_vocab * vocab_tgt = llama_model_get_vocab(model_tgt); const llama_vocab * vocab_dft = llama_model_get_vocab(model_dft); @@ -242,7 +242,7 @@ int main(int argc, char ** argv) { bool accept = false; if (params.sampling.temp > 0) { // stochastic verification - common_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft], true); + common_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft]); auto & dist_tgt = *common_sampler_get_candidates(smpl, true); @@ -491,7 +491,7 @@ int main(int argc, char ** argv) { continue; } - common_sampler_sample(drafts[s].smpl, ctx_dft, drafts[s].i_batch_dft, true); + common_sampler_sample(drafts[s].smpl, ctx_dft, drafts[s].i_batch_dft); const auto * cur_p = common_sampler_get_candidates(drafts[s].smpl, true); diff --git a/examples/training/finetune.cpp b/examples/training/finetune.cpp index 416d8d8f6c..c82de8d35d 100644 --- a/examples/training/finetune.cpp +++ b/examples/training/finetune.cpp @@ -39,9 +39,10 @@ int main(int argc, char ** argv) { llama_backend_init(); llama_numa_init(params.numa); // load the model and apply lora adapter, if any - common_init_result llama_init = common_init_from_params(params); - llama_model_ptr & model = llama_init.model; - llama_context_ptr & ctx = llama_init.context; + auto llama_init = common_init_from_params(params); + + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); if (model == NULL) { LOG_ERR("%s: unable to load model\n", __func__); @@ -54,8 +55,8 @@ int main(int argc, char ** argv) { LOG_INF("%s\n", common_params_get_system_info(params).c_str()); } - std::vector tokens = common_tokenize(ctx.get(), params.prompt, true); - ggml_opt_dataset_t dataset = common_opt_dataset_init(ctx.get(), tokens, llama_n_ctx(ctx.get()) / 2); + std::vector tokens = common_tokenize(ctx, params.prompt, true); + ggml_opt_dataset_t dataset = common_opt_dataset_init(ctx, tokens, llama_n_ctx(ctx) / 2); struct lr_opt & lr = params.lr; LOG_INF("-optimizer %s -lr0 %.2g -wd %.2g -lr-min %.2g -min-epochs %.2g -epochs %d -period %.2g -val %.2g\n", @@ -70,7 +71,7 @@ int main(int argc, char ** argv) { /*get_opt_pars_ud =*/¶ms.lr, /*optimizer_type =*/params.optimizer, }; - llama_opt_init(ctx.get(), model.get(), lopt_params); + llama_opt_init(ctx, model, lopt_params); const int64_t idata_split = ggml_opt_dataset_ndata(dataset) * (1.0f - params.val_split); @@ -78,7 +79,7 @@ int main(int argc, char ** argv) { ggml_opt_result_t result_eval = ggml_opt_result_init(); for (lr.epoch = 0; lr.epoch < lr.epochs; ++lr.epoch) { - llama_opt_epoch(ctx.get(), dataset, result_train, result_eval, idata_split, + llama_opt_epoch(ctx, dataset, result_train, result_eval, idata_split, ggml_opt_epoch_callback_progress_bar, ggml_opt_epoch_callback_progress_bar); fprintf(stderr, "\n"); @@ -88,7 +89,7 @@ int main(int argc, char ** argv) { ggml_opt_result_free(result_train); ggml_opt_result_free(result_eval); - llama_model_save_to_file(model.get(), params.out_file.c_str()); + llama_model_save_to_file(model, params.out_file.c_str()); llama_backend_free(); diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt index ab5b4760e2..a65dcfbe1e 100644 --- a/ggml/CMakeLists.txt +++ b/ggml/CMakeLists.txt @@ -54,6 +54,10 @@ if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR) # TODO else() set(GGML_STANDALONE OFF) + + if (NOT CMAKE_RUNTIME_OUTPUT_DIRECTORY) + set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin) + endif() endif() if (EMSCRIPTEN) diff --git a/ggml/include/ggml-alloc.h b/ggml/include/ggml-alloc.h index 2cb150fd2a..78aa059dde 100644 --- a/ggml/include/ggml-alloc.h +++ b/ggml/include/ggml-alloc.h @@ -53,7 +53,14 @@ GGML_API void ggml_gallocr_free(ggml_gallocr_t galloc); // call with a worst-case graph to avoid buffer reallocations // not strictly required for single buffer usage: ggml_gallocr_alloc_graph will reallocate the buffers automatically if needed // returns false if the buffer allocation failed +// ggml_gallocr_resrve_n_size writes the buffer sizes per galloc buffer that would be allocated by ggml_gallocr_reserve_n to sizes GGML_API bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph * graph); +GGML_API void ggml_gallocr_reserve_n_size( + ggml_gallocr_t galloc, + struct ggml_cgraph * graph, + const int * node_buffer_ids, + const int * leaf_buffer_ids, + size_t * sizes); GGML_API bool ggml_gallocr_reserve_n( ggml_gallocr_t galloc, struct ggml_cgraph * graph, @@ -68,6 +75,8 @@ GGML_API size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_i // Utils // Create a buffer and allocate all the tensors in a ggml_context +// ggml_backend_alloc_ctx_tensors_from_buft_size returns the size of the buffer that would be allocated by ggml_backend_alloc_ctx_tensors_from_buft +GGML_API size_t ggml_backend_alloc_ctx_tensors_from_buft_size(struct ggml_context * ctx, ggml_backend_buffer_type_t buft); GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft); GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, ggml_backend_t backend); diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h index f1b7407859..4ed5f35774 100644 --- a/ggml/include/ggml-backend.h +++ b/ggml/include/ggml-backend.h @@ -307,6 +307,7 @@ extern "C" { GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); // Initialize backend buffers from a measure graph + GGML_API void ggml_backend_sched_reserve_size(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph, size_t * sizes); GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); // returns success GGML_API int ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched); diff --git a/ggml/include/ggml-cpu.h b/ggml/include/ggml-cpu.h index 9edd485136..4f3b99c8d0 100644 --- a/ggml/include/ggml-cpu.h +++ b/ggml/include/ggml-cpu.h @@ -99,6 +99,7 @@ extern "C" { GGML_BACKEND_API int ggml_cpu_has_sme (void); // other GGML_BACKEND_API int ggml_cpu_has_riscv_v (void); + GGML_BACKEND_API int ggml_cpu_get_rvv_vlen (void); // risc-v vector length in bytes GGML_BACKEND_API int ggml_cpu_has_vsx (void); GGML_BACKEND_API int ggml_cpu_has_vxe (void); GGML_BACKEND_API int ggml_cpu_has_wasm_simd (void); diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 6bc762c069..20c912d0e9 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -2305,13 +2305,11 @@ extern "C" { float stop, float step); -#define GGML_KQ_MASK_PAD 1 - - // q: [n_embd_k, n_batch, n_head, ne3 ] - // k: [n_embd_k, n_kv, n_head_kv, ne3 ] - // v: [n_embd_v, n_kv, n_head_kv, ne3 ] !! not transposed !! - // mask: [n_kv, n_batch_pad, ne32, ne33] !! n_batch_pad = GGML_PAD(n_batch, GGML_KQ_MASK_PAD) !! - // res: [n_embd_v, n_head, n_batch, ne3 ] !! permuted !! + // q: [n_embd_k, n_batch, n_head, ne3 ] + // k: [n_embd_k, n_kv, n_head_kv, ne3 ] + // v: [n_embd_v, n_kv, n_head_kv, ne3 ] !! not transposed !! + // mask: [n_kv, n_batch, ne32, ne33] + // res: [n_embd_v, n_head, n_batch, ne3 ] !! permuted !! // // broadcast: // n_head % n_head_kv == 0 @@ -2617,7 +2615,8 @@ extern "C" { // Set callback for all future logging events. // If this is not called, or NULL is supplied, everything is output on stderr. - GGML_API void ggml_log_set(ggml_log_callback log_callback, void * user_data); + GGML_API void ggml_log_get(ggml_log_callback * log_callback, void ** user_data); + GGML_API void ggml_log_set(ggml_log_callback log_callback, void * user_data); GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor); diff --git a/ggml/src/ggml-alloc.c b/ggml/src/ggml-alloc.c index 218222ece8..41419b617b 100644 --- a/ggml/src/ggml-alloc.c +++ b/ggml/src/ggml-alloc.c @@ -25,6 +25,7 @@ static bool ggml_is_view(const struct ggml_tensor * t) { // ops that return true for this function must not use restrict pointers for their backend implementations bool ggml_op_can_inplace(enum ggml_op op) { switch (op) { + case GGML_OP_FILL: case GGML_OP_SCALE: case GGML_OP_DIAG_MASK_ZERO: case GGML_OP_DIAG_MASK_INF: @@ -311,16 +312,9 @@ static struct buffer_address ggml_dyn_tallocr_alloc(struct ggml_dyn_tallocr * al } // this is a very naive implementation, but for our case the number of free blocks should be very small -static void ggml_dyn_tallocr_free_tensor(struct ggml_dyn_tallocr * alloc, struct buffer_address addr, size_t size, const struct ggml_tensor * tensor) { +static void ggml_dyn_tallocr_free_bytes(struct ggml_dyn_tallocr * alloc, struct buffer_address addr, size_t size) { size = aligned_offset(NULL, size, alloc->alignment); - AT_PRINTF("%s: freeing %s at {chunk=%d, offset=%zu} (%zu bytes) - n_free_blocks = %d\n", - __func__, tensor->name, addr.chunk, addr.offset, size, alloc->chunks[addr.chunk]->n_free_blocks); - -#ifdef GGML_ALLOCATOR_DEBUG - remove_allocated_tensor(alloc, addr, tensor); -#endif - struct tallocr_chunk * chunk = alloc->chunks[addr.chunk]; // see if we can merge with an existing block @@ -356,8 +350,6 @@ static void ggml_dyn_tallocr_free_tensor(struct ggml_dyn_tallocr * alloc, struct } // otherwise, add a new block ggml_dyn_tallocr_insert_block(chunk, addr.offset, size); - - GGML_UNUSED(tensor); } static void ggml_dyn_tallocr_reset(struct ggml_dyn_tallocr * alloc) { @@ -602,7 +594,9 @@ static bool ggml_gallocr_is_own(ggml_gallocr_t galloc, struct ggml_tensor * t) { } static bool ggml_gallocr_is_allocated(ggml_gallocr_t galloc, struct ggml_tensor * t) { - return t->data != NULL || ggml_gallocr_hash_get(galloc, t)->allocated; + return t->data != NULL // tensor data already set externally + || t->buffer // tensor on external buffer (but not yet allocated) + || ggml_gallocr_is_own(galloc, t); // tensor will be allocated by galloc } // free the extra space at the end if the new tensor is smaller @@ -615,13 +609,17 @@ static void ggml_gallocr_free_extra_space(ggml_gallocr_t galloc, struct ggml_ten GGML_ASSERT(parent_size >= node_size); + // note: we want after the freeing the chunks to continue to be aligned + struct ggml_dyn_tallocr * p_alloc = galloc->buf_tallocs[p_hn->buffer_id]; + parent_size = aligned_offset(NULL, parent_size, p_alloc->alignment); + node_size = aligned_offset(NULL, node_size, p_alloc->alignment); + if (parent_size > node_size) { - struct ggml_dyn_tallocr * p_alloc = galloc->buf_tallocs[p_hn->buffer_id]; struct buffer_address p_addr = p_hn->addr; p_addr.offset += node_size; size_t extra_size = parent_size - node_size; AT_PRINTF("freeing extra %zu bytes from parent %s for %s\n", extra_size, parent->name, node->name); - ggml_dyn_tallocr_free_tensor(p_alloc, p_addr, extra_size, parent); + ggml_dyn_tallocr_free_bytes(p_alloc, p_addr, extra_size); } } @@ -705,7 +703,14 @@ static void ggml_gallocr_free_node(ggml_gallocr_t galloc, struct ggml_tensor * n struct ggml_dyn_tallocr * alloc = galloc->buf_tallocs[buffer_id]; ggml_backend_buffer_type_t buft = galloc->bufts[buffer_id]; size_t size = ggml_backend_buft_get_alloc_size(buft, node); - ggml_dyn_tallocr_free_tensor(alloc, hn->addr, size, node); + + AT_PRINTF("%s: freeing %s at {chunk=%d, offset=%zu} (%zu bytes) - n_free_blocks = %d\n", + __func__, node->name, hn->addr.chunk, hn->addr.offset, size, alloc->chunks[hn->addr.chunk]->n_free_blocks); +#ifdef GGML_ALLOCATOR_DEBUG + remove_allocated_tensor(alloc, hn->addr, node); +#endif + + ggml_dyn_tallocr_free_bytes(alloc, hn->addr, size); hn->allocated = false; } @@ -820,7 +825,8 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr } } -bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) { +static bool ggml_gallocr_reserve_n_impl( + ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids, bool no_alloc) { size_t min_hash_size = graph->n_nodes + graph->n_leafs; // add 25% margin to avoid hash collisions min_hash_size += min_hash_size / 4; @@ -925,16 +931,19 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c size_t cur_size = galloc->buffers[i] ? ggml_vbuffer_size(galloc->buffers[i]) : 0; if (cur_size > 0) { GGML_LOG_DEBUG("%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", - __func__, ggml_backend_buft_name(galloc->bufts[i]), - cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0); + __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0); } } #endif ggml_vbuffer_free(galloc->buffers[i]); - galloc->buffers[i] = ggml_vbuffer_alloc(galloc->bufts[i], galloc->buf_tallocs[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE); - if (galloc->buffers[i] == NULL) { - GGML_LOG_ERROR("%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size); - return false; + if (no_alloc) { + galloc->buffers[i] = NULL; + } else { + galloc->buffers[i] = ggml_vbuffer_alloc(galloc->bufts[i], galloc->buf_tallocs[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE); + if (galloc->buffers[i] == NULL) { + GGML_LOG_ERROR("%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size); + return false; + } } } } @@ -942,6 +951,21 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c return true; } +void ggml_gallocr_reserve_n_size( + ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids, size_t * sizes) { + GGML_ASSERT(ggml_gallocr_reserve_n_impl(galloc, graph, node_buffer_ids, leaf_buffer_ids, /*no_alloc =*/ true)); + for (int i = 0; i < galloc->n_buffers; i++) { + sizes[i] = 0; + for (int c = 0; c < galloc->buf_tallocs[i]->n_chunks; c++) { + sizes[i] += galloc->buf_tallocs[i]->chunks[c]->max_size; + } + } +} + +bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) { + return ggml_gallocr_reserve_n_impl(galloc, graph, node_buffer_ids, leaf_buffer_ids, /*no_alloc =*/ false); +} + bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) { return ggml_gallocr_reserve_n(galloc, graph, NULL, NULL); } @@ -1144,7 +1168,8 @@ static bool alloc_tensor_range(struct ggml_context * ctx, return true; } -ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) { +static ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft_impl( + struct ggml_context * ctx, ggml_backend_buffer_type_t buft, size_t * nbytes_total, bool no_alloc) { GGML_ASSERT(ggml_get_no_alloc(ctx) == true); size_t alignment = ggml_backend_buft_get_alignment(buft); @@ -1152,6 +1177,7 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte ggml_backend_buffer_t * buffers = NULL; size_t n_buffers = 0; + *nbytes_total = 0; size_t cur_buf_size = 0; struct ggml_tensor * first = ggml_get_first_tensor(ctx); @@ -1163,10 +1189,11 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte if (cur_buf_size > 0 && (cur_buf_size + this_size) > max_size) { // allocate tensors in the current buffer - if (!alloc_tensor_range(ctx, first, t, buft, cur_buf_size, &buffers, &n_buffers)) { + if (!no_alloc && !alloc_tensor_range(ctx, first, t, buft, cur_buf_size, &buffers, &n_buffers)) { return NULL; } first = t; + *nbytes_total += cur_buf_size; cur_buf_size = this_size; } else { cur_buf_size += this_size; @@ -1175,15 +1202,21 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte // allocate remaining tensors if (cur_buf_size > 0) { - if (!alloc_tensor_range(ctx, first, NULL, buft, cur_buf_size, &buffers, &n_buffers)) { + *nbytes_total += cur_buf_size; + if (!no_alloc && !alloc_tensor_range(ctx, first, NULL, buft, cur_buf_size, &buffers, &n_buffers)) { return NULL; } } + if (no_alloc) { + return NULL; + } + if (n_buffers == 0) { #ifndef NDEBUG GGML_LOG_DEBUG("%s: all tensors in the context are already allocated\n", __func__); #endif + GGML_ASSERT(!buffers); return NULL; } @@ -1193,10 +1226,24 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte } else { buffer = ggml_backend_multi_buffer_alloc_buffer(buffers, n_buffers); } - free(buffers); + if (buffers) { + free(buffers); // can be NULL if context is empty or no_alloc + } return buffer; } +size_t ggml_backend_alloc_ctx_tensors_from_buft_size(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) { + size_t nbytes_total = 0; + ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft_impl(ctx, buft, &nbytes_total, /*no_alloc=*/ true); + GGML_ASSERT(!buf); + return nbytes_total; +} + +ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) { + size_t nbytes_total = 0; + return ggml_backend_alloc_ctx_tensors_from_buft_impl(ctx, buft, &nbytes_total, /*no_alloc =*/ false); +} + ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, ggml_backend_t backend) { return ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_get_default_buffer_type(backend)); } diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index 08681f35e3..8547ecc849 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -36,12 +36,11 @@ const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) { } ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + GGML_ASSERT(buft); if (size == 0) { // return a dummy buffer for zero-sized allocations return ggml_backend_buffer_init(buft, {}, NULL, 0); } - - GGML_ASSERT(buft); return buft->iface.alloc_buffer(buft, size); } @@ -128,6 +127,12 @@ void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) { return NULL; } + // FIXME JG: a multi_buffer has a non-zero size, according to the above comment get_base is not optional, + // I don't know whether the above comment is correct + if (!buffer->iface.get_base) { + return NULL; + } + void * base = buffer->iface.get_base(buffer); GGML_ASSERT(base != NULL && "backend buffer base cannot be NULL"); @@ -1727,6 +1732,20 @@ void ggml_backend_sched_reset(ggml_backend_sched_t sched) { sched->is_alloc = false; } +void ggml_backend_sched_reserve_size(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph, size_t * sizes) { + GGML_ASSERT(sched); + GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes + measure_graph->n_leafs); + GGML_ASSERT(sizes); + + ggml_backend_sched_reset(sched); + + ggml_backend_sched_synchronize(sched); + + ggml_backend_sched_split_graph(sched, measure_graph); + + ggml_gallocr_reserve_n_size(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids, sizes); +} + bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) { GGML_ASSERT(sched); GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes + measure_graph->n_leafs); diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp index 48f4b7db69..835b53f659 100644 --- a/ggml/src/ggml-cann/aclnn_ops.cpp +++ b/ggml/src/ggml-cann/aclnn_ops.cpp @@ -2251,12 +2251,12 @@ static void aclnn_rope_cache_init(ggml_backend_cann_context & ctx, int sections[4], bool mrope_used, bool is_imrope, - bool indep_sects) { - ggml_tensor * src0 = dst->src[0]; // input + bool indep_sects, + int64_t rope_dims) { ggml_tensor * src1 = dst->src[1]; // position ggml_tensor * src2 = dst->src[2]; // freq_factors - int64_t theta_scale_length = src0->ne[0] / 2; + int64_t theta_scale_length = rope_dims / 2; int64_t position_length = dst->ne[2]; // TODO: check theta_scale_length and position_length. @@ -2331,18 +2331,17 @@ static void aclnn_rope_cache_init(ggml_backend_cann_context & ctx, ACL_CHECK(aclrtMemcpyAsync(ctx.rope_cache.theta_scale_cache, theta_scale_length * sizeof(float), ctx.rope_cache.theta_scale_exp_host, theta_scale_length * sizeof(float), ACL_MEMCPY_HOST_TO_DEVICE, ctx.stream())); - - acl_theta_scale_tensor = ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float), - theta_scale_ne, theta_scale_nb, 1); } + acl_theta_scale_tensor = ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float), + theta_scale_ne, theta_scale_nb, 1); // Step1.2: prepare rope_yarn_ramp, if this part updated, should update theta_scale_tensor. + // TODO: acl_yarn_ramp_tensor use rope cache. bool yarn_ramp_tensor_updated = false; ggml_cann_pool_alloc yarn_ramp_allocator(ctx.pool()); acl_tensor_ptr acl_yarn_ramp_tensor; - if (ext_factor != 0 && - // TODO: check more parameter. - (ctx.rope_cache.theta_scale_length != theta_scale_length || ctx.rope_cache.freq_scale != freq_scale)) { + if (ext_factor != 0 && (theta_scale_updated || ctx.rope_cache.theta_scale_length != theta_scale_length || + ctx.rope_cache.freq_scale != freq_scale)) { yarn_ramp_tensor_updated = true; // -rope_yarn_ramp @@ -2590,7 +2589,7 @@ static void aclnn_rope_cache_init(ggml_backend_cann_context & ctx, aclnn_muls(ctx, acl_cos_tensor.get(), attn_factor, nullptr, true); } - int64_t sin_reshape_ne[4] = { src0->ne[0], 1, dst->ne[2], 1 }; + int64_t sin_reshape_ne[4] = { rope_dims, 1, dst->ne[2], 1 }; size_t sin_reshape_nb[GGML_MAX_DIMS]; sin_reshape_nb[0] = sizeof(float); for (int i = 1; i < GGML_MAX_DIMS; i++) { @@ -2645,7 +2644,7 @@ void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst) { // param float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; - int sections[4]; + int sections[4]; // const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; @@ -2654,44 +2653,60 @@ void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst) { GGML_TENSOR_UNARY_OP_LOCALS - memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); - memcpy(§ions, (int32_t *) dst->op_params + 11, sizeof(int)*4); + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + memcpy(§ions, (int32_t *) dst->op_params + 11, sizeof(int) * 4); - // TODO: n_dims <= ne0 - GGML_ASSERT(n_dims == ne0); GGML_ASSERT(n_dims % 2 == 0); + GGML_ASSERT(n_dims <= ne00); const float theta_scale = powf(freq_base, -2.0f / n_dims); float corr_dims[2]; ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); - bool is_neox = mode & GGML_ROPE_TYPE_NEOX; - const bool is_imrope = mode == GGML_ROPE_TYPE_IMROPE; // qwen3vl apply interleaved mrope - const bool mrope_used = mode & GGML_ROPE_TYPE_MROPE; // ggml_rope_multi, note: also true for vision (24 & 8 == true) and for imrope - const bool is_vision = mode == GGML_ROPE_TYPE_VISION; + bool is_neox = mode & GGML_ROPE_TYPE_NEOX; + const bool is_imrope = mode == GGML_ROPE_TYPE_IMROPE; // qwen3vl apply interleaved mrope + // mrope_used means the GGML_ROPE_TYPE_MROPE bit is set. + // Note: this bit is also set for imrope and some vision modes, + // so mrope_used does NOT exclusively indicate pure mrope. + const bool mrope_used = mode & GGML_ROPE_TYPE_MROPE; + const bool is_vision = mode == GGML_ROPE_TYPE_VISION; if (mrope_used) { GGML_ASSERT(sections[0] > 0 || sections[1] > 0 || sections[2] > 0); } if (is_vision) { - GGML_ASSERT(n_dims == ne0/2); + GGML_ASSERT(n_dims == ne0 / 2); } if (is_imrope || mrope_used) { is_neox = true; } - // init ctx.rope_cos/rope_sin cache - aclnn_rope_cache_init(ctx, dst, corr_dims, ext_factor, theta_scale, freq_scale, attn_factor, is_neox, sections, mrope_used, is_imrope, is_vision); + int64_t rope_dims = n_dims; - int64_t sin_reshape_ne[4] = { ne00, 1, ne02, 1 }; + //Our current RotaryPositionEmbedding does not support the VISION mode, + //but essentially it only modifies theta_base in mrope, + //then repeats it at the end in the same way as is_neox. + //In fact, RoPE is still applied across all dimensions. + if (is_vision) { + rope_dims = src0->ne[0]; + } + int64_t tail_dims = ne00 - rope_dims; + bool has_tail = tail_dims > 0; + + // init ctx.rope_cos/rope_sin cache + aclnn_rope_cache_init(ctx, dst, corr_dims, ext_factor, theta_scale, freq_scale, attn_factor, is_neox, sections, + mrope_used, is_imrope, is_vision, rope_dims); + + // Cache is generated with ne00 dimensions, so we use ne00 for reshape + int64_t sin_reshape_ne[4] = { rope_dims, 1, ne02, 1 }; size_t sin_reshape_nb[GGML_MAX_DIMS]; sin_reshape_nb[0] = sizeof(float); for (int i = 1; i < GGML_MAX_DIMS; i++) { @@ -2704,7 +2719,6 @@ void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst) { acl_tensor_ptr acl_src = ggml_cann_create_tensor(src0); acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst); - #ifdef ASCEND_310P // Special ROPE operation for 310P @@ -2844,46 +2858,124 @@ void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst) { } return; #endif - int64_t acl_mode = is_neox ? 0 : 1; - switch (src0->type) { - case GGML_TYPE_F32: - { - GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src.get(), acl_cos_reshape_tensor.get(), - acl_sin_reshape_tensor.get(), acl_mode, acl_dst.get()); - break; - } - case GGML_TYPE_F16: - { - ggml_cann_pool_alloc src_trans_allocator(ctx.pool(), ggml_nelements(src0) * sizeof(float)); - void * src_trans_buffer = src_trans_allocator.get(); - ggml_cann_pool_alloc dst_trans_allocator(ctx.pool(), ggml_nelements(dst) * sizeof(float)); - void * dst_trans_buffer = dst_trans_allocator.get(); + // Pre-define head and tail dimensions for reuse + int64_t head_ne[GGML_MAX_DIMS] = { rope_dims, ne01, ne02, ne03 }; + int64_t tail_ne[GGML_MAX_DIMS] = { tail_dims, ne01, ne02, ne03 }; - size_t src_trans_nb[GGML_MAX_DIMS]; - src_trans_nb[0] = sizeof(float); - for (int i = 1; i < GGML_MAX_DIMS; i++) { - src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1]; - } + // Step 1: Prepare trans tensors for F16 type conversion to F32 if needed + bool src_dst_need_trans = false; + ggml_cann_pool_alloc src_trans_allocator(ctx.pool()); + ggml_cann_pool_alloc dst_trans_allocator(ctx.pool()); + acl_tensor_ptr acl_src_trans_tensor; + acl_tensor_ptr acl_dst_trans_tensor; + void * src_trans_buffer = nullptr; + void * dst_trans_buffer = nullptr; + size_t src_dst_trans_nb[GGML_MAX_DIMS]; + if (src0->type == GGML_TYPE_F16) { + src_dst_need_trans = true; + src_trans_buffer = src_trans_allocator.alloc(ggml_nelements(src0) * sizeof(float)); + dst_trans_buffer = dst_trans_allocator.alloc(ggml_nelements(dst) * sizeof(float)); - acl_tensor_ptr acl_src_trans_tensor = ggml_cann_create_tensor( - src_trans_buffer, ACL_FLOAT, sizeof(float), src0->ne, src_trans_nb, GGML_MAX_DIMS); - acl_tensor_ptr acl_dst_trans_tensor = ggml_cann_create_tensor( - dst_trans_buffer, ACL_FLOAT, sizeof(float), dst->ne, src_trans_nb, GGML_MAX_DIMS); + src_dst_trans_nb[0] = sizeof(float); + for (int i = 1; i < GGML_MAX_DIMS; i++) { + src_dst_trans_nb[i] = src_dst_trans_nb[i - 1] * src0->ne[i - 1]; + } + acl_src_trans_tensor = ggml_cann_create_tensor(src_trans_buffer, ACL_FLOAT, sizeof(float), src0->ne, + src_dst_trans_nb, GGML_MAX_DIMS); + acl_dst_trans_tensor = ggml_cann_create_tensor(dst_trans_buffer, ACL_FLOAT, sizeof(float), dst->ne, + src_dst_trans_nb, GGML_MAX_DIMS); + aclnn_cast(ctx, acl_src.get(), acl_src_trans_tensor.get(), ACL_FLOAT); + } - aclnn_cast(ctx, acl_src.get(), acl_src_trans_tensor.get(), ACL_FLOAT); + // Step 2: Prepare head tensors for tail splitting if needed + acl_tensor_ptr acl_src_head; + acl_tensor_ptr acl_dst_head; + if (has_tail) { + // Create head views for RotaryPositionEmbedding (only first rope_dims dimensions) + // RotaryPositionEmbedding requires contiguous dst tensor, so we use a temporary buffer + if (src_dst_need_trans) { + // Use F32 trans tensor strides + acl_src_head = ggml_cann_create_tensor((char *) src_trans_buffer, ACL_FLOAT, sizeof(float), head_ne, + src_dst_trans_nb, GGML_MAX_DIMS); + } else { + // Use original F32 tensor strides + acl_src_head = ggml_cann_create_tensor((char *) src0->data, ACL_FLOAT, sizeof(float), head_ne, src0->nb, + GGML_MAX_DIMS); + } - GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src_trans_tensor.get(), - acl_cos_reshape_tensor.get(), acl_sin_reshape_tensor.get(), acl_mode, - acl_dst_trans_tensor.get()); + int64_t head_elements = rope_dims * ne01 * ne02 * ne03; + ggml_cann_pool_alloc dst_head_contiguous_allocator(ctx.pool(), head_elements * sizeof(float)); + void * dst_head_contiguous_buffer = dst_head_contiguous_allocator.get(); - aclnn_cast(ctx, acl_dst_trans_tensor.get(), acl_dst.get(), ACL_FLOAT16); - break; - } - default: - GGML_ABORT("Unsupported tensor type for GGML_OP_ROPE"); - break; + size_t head_contiguous_nb[GGML_MAX_DIMS]; + head_contiguous_nb[0] = sizeof(float); + for (int i = 1; i < GGML_MAX_DIMS; i++) { + head_contiguous_nb[i] = head_contiguous_nb[i - 1] * head_ne[i - 1]; + } + acl_dst_head = ggml_cann_create_tensor(dst_head_contiguous_buffer, ACL_FLOAT, sizeof(float), head_ne, + head_contiguous_nb, GGML_MAX_DIMS); + } + + // Step 3: Execute RotaryPositionEmbedding + if (has_tail) { + // Rotate only the head portion (first rope_dims dimensions) + GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src_head.get(), acl_cos_reshape_tensor.get(), + acl_sin_reshape_tensor.get(), acl_mode, acl_dst_head.get()); + + // Copy head result from contiguous buffer back to destination tensor + if (src_dst_need_trans) { + acl_tensor_ptr acl_dst_head_target = ggml_cann_create_tensor( + (char *) dst_trans_buffer, ACL_FLOAT, sizeof(float), head_ne, src_dst_trans_nb, GGML_MAX_DIMS); + cann_copy(ctx, acl_dst_head.get(), acl_dst_head_target.get()); + } else { + acl_tensor_ptr acl_dst_head_target = + ggml_cann_create_tensor((char *) dst->data, ACL_FLOAT, sizeof(float), head_ne, dst->nb, GGML_MAX_DIMS); + cann_copy(ctx, acl_dst_head.get(), acl_dst_head_target.get()); + } + } else if (src_dst_need_trans) { + // Rotate full tensor (no tail), using trans tensors + GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src_trans_tensor.get(), acl_cos_reshape_tensor.get(), + acl_sin_reshape_tensor.get(), acl_mode, acl_dst_trans_tensor.get()); + } else { + // Rotate full tensor (no tail), using original tensors + GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src.get(), acl_cos_reshape_tensor.get(), + acl_sin_reshape_tensor.get(), acl_mode, acl_dst.get()); + } + + // Step 4: Copy unrotated tail portion from source to destination + if (has_tail) { + size_t src_tail_offset; + size_t dst_tail_offset; + + auto copy_tail_device = [&](void * src_ptr, void * dst_ptr, aclDataType dtype, size_t elem_size, + size_t * nb_src_arr, size_t * nb_dst_arr) { + acl_tensor_ptr acl_src_tail = + ggml_cann_create_tensor(src_ptr, dtype, elem_size, tail_ne, nb_src_arr, GGML_MAX_DIMS); + acl_tensor_ptr acl_dst_tail = + ggml_cann_create_tensor(dst_ptr, dtype, elem_size, tail_ne, nb_dst_arr, GGML_MAX_DIMS); + cann_copy(ctx, acl_src_tail.get(), acl_dst_tail.get()); + }; + + if (src_dst_need_trans) { + // Use F32 trans tensor strides and offsets + src_tail_offset = rope_dims * src_dst_trans_nb[0]; + dst_tail_offset = rope_dims * src_dst_trans_nb[0]; + copy_tail_device((char *) src_trans_buffer + src_tail_offset, (char *) dst_trans_buffer + dst_tail_offset, + ACL_FLOAT, sizeof(float), src_dst_trans_nb, src_dst_trans_nb); + } else { + // Use original tensor strides and offsets + src_tail_offset = rope_dims * nb00; + dst_tail_offset = rope_dims * nb0; + copy_tail_device((char *) src0->data + src_tail_offset, (char *) dst->data + dst_tail_offset, + ggml_cann_type_mapping(dst->type), ggml_element_size(dst), src0->nb, dst->nb); + } + } + + // Step 5: Cast back to F16 if needed + if (src_dst_need_trans) { + aclnn_cast(ctx, acl_dst_trans_tensor.get(), acl_dst.get(), ACL_FLOAT16); } } diff --git a/ggml/src/ggml-cann/common.h b/ggml/src/ggml-cann/common.h index b17445bb9a..45c7294e68 100644 --- a/ggml/src/ggml-cann/common.h +++ b/ggml/src/ggml-cann/common.h @@ -315,7 +315,7 @@ struct ggml_cann_rope_cache { if (theta_scale_exp_host) { free(theta_scale_exp_host); } - if(position_select_index_host) { + if (position_select_index_host) { free(position_select_index_host); } } @@ -340,7 +340,7 @@ struct ggml_cann_rope_cache { void set(int64_t theta_scale_length, int64_t position_length, - float ext_factor, + float ext_factor, float theta_scale, float freq_scale, float attn_factor, diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp index 23dc0433c1..da624c587c 100644 --- a/ggml/src/ggml-cann/ggml-cann.cpp +++ b/ggml/src/ggml-cann/ggml-cann.cpp @@ -2308,7 +2308,7 @@ static enum ggml_status ggml_backend_cann_graph_compute(ggml_backend_t backend, bool cann_graph_update_required = false; #ifdef USE_ACL_GRAPH - bool use_cann_graph = true; + bool use_cann_graph = true; static bool prefill_use_graph = parse_bool(get_env("GGML_CANN_PREFILL_USE_GRAPH").value_or("")); if (!prefill_use_graph) { @@ -2338,7 +2338,7 @@ static enum ggml_status ggml_backend_cann_graph_compute(ggml_backend_t backend, } } #else - bool use_cann_graph = false; + bool use_cann_graph = false; #endif // USE_ACL_GRAPH evaluate_and_capture_cann_graph(cann_ctx, cgraph, use_cann_graph, cann_graph_update_required); @@ -2474,16 +2474,14 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten } case GGML_OP_ROPE: { - // TODO: with ops-test v == 1 - // TODO: n_dims <= ne0 - if (op->src[0]->ne[0] != op->op_params[1]) { - return false; - } - if (op->src[0]->ne[0] > 896) { return false; } #ifdef ASCEND_310P + // TODO: Support rope_dim < ne00(dim) + if (op->src[0]->ne[0] != op->op_params[1]) { + return false; + } if (!ggml_is_contiguous(op->src[0])) { return false; } @@ -2550,6 +2548,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten case GGML_OP_ARGSORT: case GGML_OP_ACC: case GGML_OP_GROUP_NORM: + return true; case GGML_OP_PAD: // TODO: add circular padding support for cann, see https://github.com/ggml-org/llama.cpp/pull/16985 return ggml_get_op_params_i32(op, 8) == 0; diff --git a/ggml/src/ggml-cpu/arch/arm/repack.cpp b/ggml/src/ggml-cpu/arch/arm/repack.cpp index 683ed8d2df..fb7f074a85 100644 --- a/ggml/src/ggml-cpu/arch/arm/repack.cpp +++ b/ggml/src/ggml-cpu/arch/arm/repack.cpp @@ -24,6 +24,7 @@ #define UNUSED GGML_UNUSED +#if defined(__aarch64__) && defined(__ARM_NEON) && (defined(__ARM_FEATURE_MATMUL_INT8) || defined(__ARM_FEATURE_DOTPROD)) static inline void decode_q4_Kx8_scales_mins(const uint8_t * scales_in, int16x8_t * out_mins, int8_t * out_scales) { @@ -46,6 +47,7 @@ static inline void decode_q4_Kx8_scales_mins(const uint8_t * scales_in, scales_u32[1] = (sm[2] & kmask2) | (((sm[0] >> 6) & kmask3) << 4); memcpy(out_scales, scales_u32, 8); } +#endif void ggml_quantize_mat_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) { assert(QK8_0 == 32); diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index b468b115a1..a59b518938 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -81,6 +81,11 @@ struct ggml_arm_arch_features_type { } ggml_arm_arch_features = { 0 }; #endif +#if defined(__riscv) +struct ggml_riscv_arch_features_type { + int rvv_vlen; +} ggml_riscv_arch_features = { 0 }; +#endif #if defined(_WIN32) @@ -187,6 +192,9 @@ typedef void * thread_ret_t; typedef pthread_t ggml_thread_t; +#define GGML_THREADPOOL_N_THREADS_MASK (0xffffU) +#define GGML_THREADPOOL_N_THREADS_BITS (16) + #if defined(__APPLE__) #include #include @@ -449,7 +457,7 @@ struct ggml_threadpool { struct ggml_cplan * cplan; // synchronization primitives - atomic_int n_graph; // incremented when there is work to be done (i.e each graph) + atomic_int n_graph; // updated when there is work to be done (i.e each graph) holds graph and active thread counts. atomic_int GGML_CACHE_ALIGN n_barrier; atomic_int GGML_CACHE_ALIGN n_barrier_passed; atomic_int GGML_CACHE_ALIGN current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads. @@ -457,12 +465,10 @@ struct ggml_threadpool { // these are atomic as an annotation for thread-sanitizer atomic_bool stop; // Used for stopping the threadpool altogether atomic_bool pause; // Used for pausing the threadpool or individual threads - atomic_int abort; // Used for aborting processing of a graph + atomic_int abort; // Used for aborting processing of a graph struct ggml_compute_state * workers; // per thread state - int n_threads_max; // number of threads in the pool - atomic_int n_threads_cur; // number of threads used in the current graph - + int n_threads; // Number of threads in the pool int32_t prio; // Scheduling priority uint32_t poll; // Polling level (0 - no polling) @@ -539,7 +545,7 @@ struct ggml_state { static struct ggml_state g_state = {0}; void ggml_barrier(struct ggml_threadpool * tp) { - int n_threads = atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed); + int n_threads = atomic_load_explicit(&tp->n_graph, memory_order_relaxed) & GGML_THREADPOOL_N_THREADS_MASK; if (n_threads == 1) { return; } @@ -556,7 +562,7 @@ void ggml_barrier(struct ggml_threadpool * tp) { // last thread atomic_store_explicit(&tp->n_barrier, 0, memory_order_relaxed); - // exit barrier (fill seq-cst fence) + // exit barrier (full seq-cst fence) atomic_fetch_add_explicit(&tp->n_barrier_passed, 1, memory_order_seq_cst); return; } @@ -702,6 +708,15 @@ static void ggml_init_arm_arch_features(void) {} #endif #endif // __ARM_ARCH +#if defined(__riscv) && defined(__riscv_v_intrinsic) +#include +static void ggml_init_riscv_arch_features(void) { + ggml_riscv_arch_features.rvv_vlen = __riscv_vlenb(); +} +#else +static void ggml_init_riscv_arch_features(void) {} +#endif + struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) { GGML_ASSERT(!ggml_get_no_alloc(ctx)); @@ -2628,7 +2643,7 @@ static void ggml_thread_cpumask_next(const bool * global_mask, bool * local_mask void ggml_threadpool_free(struct ggml_threadpool* threadpool) { if (!threadpool) return; - const int n_threads = threadpool->n_threads_max; + const int n_threads = threadpool->n_threads; #ifndef GGML_USE_OPENMP struct ggml_compute_state* workers = threadpool->workers; @@ -2704,7 +2719,7 @@ struct ggml_cplan ggml_graph_plan( //GGML_PRINT_DEBUG("Threadpool is not specified. Will create a disposable threadpool : n_threads %d\n", n_threads); } if (n_threads <= 0) { - n_threads = threadpool ? threadpool->n_threads_max : GGML_DEFAULT_N_THREADS; + n_threads = threadpool ? threadpool->n_threads : GGML_DEFAULT_N_THREADS; } #if defined(__EMSCRIPTEN__) && !defined(__EMSCRIPTEN_PTHREADS__) @@ -2912,12 +2927,14 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { struct ggml_compute_params params = { /*.ith =*/ state->ith, - /*.nth =*/ atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed), + /*.nth =*/ atomic_load_explicit(&tp->n_graph, memory_order_relaxed) & GGML_THREADPOOL_N_THREADS_MASK, /*.wsize =*/ cplan->work_size, /*.wdata =*/ cplan->work_data, /*.threadpool=*/ tp, }; + GGML_PRINT_DEBUG("thread #%d compute-start cplan %p last-graph %d \n", state->ith, cplan, state->last_graph); + for (int node_n = 0; node_n < cgraph->n_nodes && atomic_load_explicit(&tp->abort, memory_order_relaxed) != node_n; node_n++) { struct ggml_tensor * node = cgraph->nodes[node_n]; @@ -2939,6 +2956,8 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { } } + GGML_PRINT_DEBUG("thread #%d compute-done cplan %p last-graph %d \n", state->ith, cplan, state->last_graph); + ggml_barrier(state->threadpool); return 0; @@ -2946,27 +2965,23 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { #ifndef GGML_USE_OPENMP -// check if thread is active -static inline bool ggml_graph_compute_thread_active(struct ggml_compute_state * state) { - struct ggml_threadpool * threadpool = state->threadpool; - int n_threads = atomic_load_explicit(&threadpool->n_threads_cur, memory_order_relaxed); - return (state->ith < n_threads); -} - // check if thread is ready to proceed (exit from polling or sleeping) +// returns true if loops should exit, sets state->pending to indicate new work static inline bool ggml_graph_compute_thread_ready(struct ggml_compute_state * state) { struct ggml_threadpool * threadpool = state->threadpool; if (state->pending || threadpool->stop || threadpool->pause) { return true; } // check for new graph/work - int new_graph = atomic_load_explicit(&threadpool->n_graph, memory_order_relaxed); - if (new_graph != state->last_graph) { - state->pending = ggml_graph_compute_thread_active(state); - state->last_graph = new_graph; + int n_graph = atomic_load_explicit(&threadpool->n_graph, memory_order_relaxed); + int n_threads = n_graph & GGML_THREADPOOL_N_THREADS_MASK; + if (n_graph != state->last_graph) { + state->pending = (state->ith < n_threads); + state->last_graph = n_graph; + return true; } - return state->pending; + return false; } // sync thread state after polling @@ -2983,11 +2998,6 @@ static inline void ggml_graph_compute_thread_sync(struct ggml_compute_state * st static inline bool ggml_graph_compute_poll_for_work(struct ggml_compute_state * state) { struct ggml_threadpool * threadpool = state->threadpool; - // Skip polling for unused threads - if (!ggml_graph_compute_thread_active(state)) { - return state->pending; - } - // This seems to make 0 ... 100 a decent range for polling level across modern processors. // Perhaps, we can adjust it dynamically based on load and things. const uint64_t n_rounds = 1024UL * 128 * threadpool->poll; @@ -3049,7 +3059,6 @@ static thread_ret_t ggml_graph_compute_secondary_thread(void* data) { ggml_graph_compute_check_for_work(state); if (state->pending) { state->pending = false; - ggml_graph_compute_thread(state); } } @@ -3064,14 +3073,15 @@ static void ggml_graph_compute_kickoff(struct ggml_threadpool * threadpool, int ggml_mutex_lock(&threadpool->mutex); - GGML_PRINT_DEBUG("threadpool: n_threads_cur %d n_threads %d\n", threadpool->n_threads_cur, n_threads); + // Update the number of active threads and the graph count + int n_graph = atomic_load_explicit(&threadpool->n_graph, memory_order_relaxed) >> GGML_THREADPOOL_N_THREADS_BITS; + n_graph = ((n_graph + 1) << GGML_THREADPOOL_N_THREADS_BITS) | (n_threads & GGML_THREADPOOL_N_THREADS_MASK); - // Update the number of active threads - atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed); + GGML_PRINT_DEBUG("compute-kickoff: n_threads %d n_graph %d\n", n_threads, n_graph); // Indicate the graph is ready to be processed // We need the full seq-cst fence here because of the polling threads (used in thread_sync) - atomic_fetch_add_explicit(&threadpool->n_graph, 1, memory_order_seq_cst); + atomic_store_explicit(&threadpool->n_graph, n_graph, memory_order_seq_cst); if (threadpool->pause) { // Update main thread prio and affinity to match the threadpool settings @@ -3109,8 +3119,7 @@ static struct ggml_threadpool * ggml_threadpool_new_impl( threadpool->pause = tpp->paused; threadpool->abort = -1; threadpool->workers = NULL; - threadpool->n_threads_max = tpp->n_threads; - threadpool->n_threads_cur = tpp->n_threads; + threadpool->n_threads = tpp->n_threads; threadpool->poll = tpp->poll; threadpool->prio = tpp->prio; threadpool->ec = GGML_STATUS_SUCCESS; @@ -3205,7 +3214,7 @@ enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cpl { // update the number of threads from the actual number of threads that we got from OpenMP n_threads = omp_get_num_threads(); - atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed); + atomic_store_explicit(&threadpool->n_graph, n_threads, memory_order_relaxed); } // Apply thread CPU mask and priority @@ -3218,13 +3227,13 @@ enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cpl ggml_graph_compute_thread(&threadpool->workers[ith]); } } else { - atomic_store_explicit(&threadpool->n_threads_cur, 1, memory_order_relaxed); + atomic_store_explicit(&threadpool->n_graph, 1, memory_order_relaxed); ggml_graph_compute_thread(&threadpool->workers[0]); } #else - if (n_threads > threadpool->n_threads_max) { - GGML_LOG_WARN("cplan requested more threads (%d) than available (%d)\n", n_threads, threadpool->n_threads_max); - n_threads = threadpool->n_threads_max; + if (n_threads > threadpool->n_threads) { + GGML_LOG_WARN("cplan requested more threads (%d) than available (%d)\n", n_threads, threadpool->n_threads); + n_threads = threadpool->n_threads; } // Kick all threads to start the new graph @@ -3464,6 +3473,14 @@ int ggml_cpu_has_riscv_v(void) { #endif } +int ggml_cpu_get_rvv_vlen(void) { +#if defined(__riscv) && defined(__riscv_v_intrinsic) + return ggml_riscv_arch_features.rvv_vlen; +#else + return 0; +#endif +} + int ggml_cpu_has_f16c(void) { #if defined(__F16C__) return 1; @@ -3630,6 +3647,10 @@ void ggml_cpu_init(void) { ggml_init_arm_arch_features(); #endif +#if defined(__riscv) + ggml_init_riscv_arch_features(); +#endif + is_first_call = false; } diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp index 3191faaa4c..f4713a4218 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.cpp +++ b/ggml/src/ggml-cpu/ggml-cpu.cpp @@ -583,6 +583,10 @@ static ggml_backend_feature * ggml_backend_cpu_get_features(ggml_backend_reg_t r if (ggml_cpu_has_riscv_v()) { features.push_back({ "RISCV_V", "1" }); } + if (ggml_cpu_get_rvv_vlen() > 0) { + static std::string rvv_vlen = std::to_string(ggml_cpu_get_rvv_vlen()); + features.push_back({ "RVV_VLEN", rvv_vlen.c_str() }); + } if (ggml_cpu_has_vsx()) { features.push_back({ "VSX", "1" }); } diff --git a/ggml/src/ggml-cpu/repack.cpp b/ggml/src/ggml-cpu/repack.cpp index 9f0d449bd6..b70ea7d78b 100644 --- a/ggml/src/ggml-cpu/repack.cpp +++ b/ggml/src/ggml-cpu/repack.cpp @@ -2169,7 +2169,8 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons static const ggml::cpu::repack::tensor_traits iq4_nl_8x8_q8_0; if (cur->type == GGML_TYPE_Q4_0) { - if (ggml_cpu_has_avx2() || (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0)) { + if (ggml_cpu_has_avx2() || (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0) + || (ggml_cpu_has_riscv_v() && (ggml_cpu_get_rvv_vlen() >= QK4_0))) { if (cur->ne[1] % 8 == 0) { return &q4_0_8x8_q8_0; } diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh index c4529f5d94..9fcb2f9fd2 100644 --- a/ggml/src/ggml-cuda/common.cuh +++ b/ggml/src/ggml-cuda/common.cuh @@ -67,19 +67,22 @@ #define GGML_CUDA_CC_RDNA1 (GGML_CUDA_CC_OFFSET_AMD + 0x1010) // RX 5000 #define GGML_CUDA_CC_RDNA2 (GGML_CUDA_CC_OFFSET_AMD + 0x1030) // RX 6000, minimum for dp4a #define GGML_CUDA_CC_RDNA3 (GGML_CUDA_CC_OFFSET_AMD + 0x1100) // RX 7000, minimum for WMMA +#define GGML_CUDA_CC_RDNA3_5 (GGML_CUDA_CC_OFFSET_AMD + 0x1150) // AI 370, AI Max 395 laptops. #define GGML_CUDA_CC_RDNA4 (GGML_CUDA_CC_OFFSET_AMD + 0x1200) // RX 9000 -#define GGML_CUDA_CC_IS_AMD(cc) (cc >= GGML_CUDA_CC_OFFSET_AMD) -#define GGML_CUDA_CC_IS_RDNA(cc) (cc >= GGML_CUDA_CC_RDNA1) -#define GGML_CUDA_CC_IS_RDNA1(cc) (cc >= GGML_CUDA_CC_RDNA1 && cc < GGML_CUDA_CC_RDNA2) -#define GGML_CUDA_CC_IS_RDNA2(cc) (cc >= GGML_CUDA_CC_RDNA2 && cc < GGML_CUDA_CC_RDNA3) -#define GGML_CUDA_CC_IS_RDNA3(cc) (cc >= GGML_CUDA_CC_RDNA3 && cc < GGML_CUDA_CC_RDNA4) -#define GGML_CUDA_CC_IS_RDNA4(cc) (cc >= GGML_CUDA_CC_RDNA4) -#define GGML_CUDA_CC_IS_GCN(cc) (cc > GGML_CUDA_CC_OFFSET_AMD && cc < GGML_CUDA_CC_CDNA1) -#define GGML_CUDA_CC_IS_CDNA(cc) (cc >= GGML_CUDA_CC_CDNA1 && cc < GGML_CUDA_CC_RDNA1) -#define GGML_CUDA_CC_IS_CDNA1(cc) (cc >= GGML_CUDA_CC_CDNA1 && cc < GGML_CUDA_CC_CDNA2) -#define GGML_CUDA_CC_IS_CDNA2(cc) (cc >= GGML_CUDA_CC_CDNA2 && cc < GGML_CUDA_CC_CDNA3) -#define GGML_CUDA_CC_IS_CDNA3(cc) (cc >= GGML_CUDA_CC_CDNA3 && cc < GGML_CUDA_CC_RDNA1) +#define GGML_CUDA_CC_IS_AMD(cc) (cc >= GGML_CUDA_CC_OFFSET_AMD) +#define GGML_CUDA_CC_IS_RDNA(cc) (cc >= GGML_CUDA_CC_RDNA1) +#define GGML_CUDA_CC_IS_RDNA1(cc) (cc >= GGML_CUDA_CC_RDNA1 && cc < GGML_CUDA_CC_RDNA2) +#define GGML_CUDA_CC_IS_RDNA2(cc) (cc >= GGML_CUDA_CC_RDNA2 && cc < GGML_CUDA_CC_RDNA3) +#define GGML_CUDA_CC_IS_RDNA3_0(cc) (cc >= GGML_CUDA_CC_RDNA3 && cc < GGML_CUDA_CC_RDNA3_5) +#define GGML_CUDA_CC_IS_RDNA3_5(cc) (cc >= GGML_CUDA_CC_RDNA3_5 && cc < GGML_CUDA_CC_RDNA4) +#define GGML_CUDA_CC_IS_RDNA3(cc) (GGML_CUDA_CC_IS_RDNA3_0(cc) || GGML_CUDA_CC_IS_RDNA3_5(cc)) +#define GGML_CUDA_CC_IS_RDNA4(cc) (cc >= GGML_CUDA_CC_RDNA4) +#define GGML_CUDA_CC_IS_GCN(cc) (cc > GGML_CUDA_CC_OFFSET_AMD && cc < GGML_CUDA_CC_CDNA1) +#define GGML_CUDA_CC_IS_CDNA(cc) (cc >= GGML_CUDA_CC_CDNA1 && cc < GGML_CUDA_CC_RDNA1) +#define GGML_CUDA_CC_IS_CDNA1(cc) (cc >= GGML_CUDA_CC_CDNA1 && cc < GGML_CUDA_CC_CDNA2) +#define GGML_CUDA_CC_IS_CDNA2(cc) (cc >= GGML_CUDA_CC_CDNA2 && cc < GGML_CUDA_CC_CDNA3) +#define GGML_CUDA_CC_IS_CDNA3(cc) (cc >= GGML_CUDA_CC_CDNA3 && cc < GGML_CUDA_CC_RDNA1) // Moore Threads #define MUSART_HMASK 40300 // MUSA rc4.3, min. ver. for half2 -> uint mask comparisons diff --git a/ggml/src/ggml-cuda/diag.cu b/ggml/src/ggml-cuda/diag.cu new file mode 100644 index 0000000000..5cea210517 --- /dev/null +++ b/ggml/src/ggml-cuda/diag.cu @@ -0,0 +1,77 @@ +#include "convert.cuh" +#include "diag.cuh" +#include "ggml.h" + +template +static __global__ void diag_kernel(T * __restrict__ dst, + const T * __restrict__ src, + const int64_t ne0, + const int64_t ne1, + const int64_t ne2, + const int64_t ne3, + const int64_t total_elements) { + const int64_t global_idx = blockIdx.x * blockDim.x + threadIdx.x; + + if (global_idx >= total_elements) { + return; + } + + const int64_t i0 = global_idx % ne0; + const int64_t i1 = (global_idx / ne0) % ne1; + const int64_t i2 = (global_idx / (ne0 * ne1)) % ne2; + const int64_t i3 = global_idx / (ne0 * ne1 * ne2); + + const int64_t dst_idx = ((i3 * ne2 + i2) * ne1 + i1) * ne0 + i0; + + if (i0 == i1) { + const int64_t batch_idx = i3 * ne2 + i2; + const int64_t src_idx = batch_idx * ne0 + i0; + dst[dst_idx] = src[src_idx]; + } else { + dst[dst_idx] = ggml_cuda_cast(0); + } + GGML_UNUSED_VARS(ne3); +} + +void ggml_cuda_op_diag(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + + void * dst_d = dst->data; + const void * src0_d = src0->data; + + cudaStream_t stream = ctx.stream(); + + GGML_ASSERT(ggml_is_contiguous(dst)); + GGML_ASSERT(ggml_is_contiguous(src0)); + + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + const int64_t ne02 = src0->ne[2]; + const int64_t ne03 = src0->ne[3]; + + const int64_t ne0 = dst->ne[0]; + const int64_t ne1 = dst->ne[1]; + const int64_t ne2 = dst->ne[2]; + const int64_t ne3 = dst->ne[3]; + + GGML_ASSERT(ne00 == ne0); + GGML_ASSERT(ne01 == 1); + GGML_ASSERT(ne02 == ne2); + GGML_ASSERT(ne03 == ne3); + + const int64_t n_elems = ggml_nelements(dst); + const int64_t num_blocks = (n_elems + CUDA_DIAG_BLOCK_SIZE - 1) / CUDA_DIAG_BLOCK_SIZE; + + switch (dst->type) { + case GGML_TYPE_F32: + diag_kernel<<>>((float *) dst_d, (const float *) src0_d, ne0, + ne1, ne2, ne3, n_elems); + break; + case GGML_TYPE_F16: + diag_kernel<<>>((half *) dst_d, (const half *) src0_d, ne0, + ne1, ne2, ne3, n_elems); + break; + default: + GGML_ABORT("unsupported type"); + } +} diff --git a/ggml/src/ggml-cuda/diag.cuh b/ggml/src/ggml-cuda/diag.cuh new file mode 100644 index 0000000000..7d73e6a8eb --- /dev/null +++ b/ggml/src/ggml-cuda/diag.cuh @@ -0,0 +1,5 @@ +#include "common.cuh" + +#define CUDA_DIAG_BLOCK_SIZE 256 + +void ggml_cuda_op_diag(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh index 2750117aa9..8dc82a9d3b 100644 --- a/ggml/src/ggml-cuda/fattn-common.cuh +++ b/ggml/src/ggml-cuda/fattn-common.cuh @@ -642,8 +642,8 @@ static __global__ void flash_attn_stream_k_fixup( const int iter_k = (ne11 + (nbatch_fa - 1)) / nbatch_fa; const int iter_j = (ne01 + (ncols1 - 1)) / ncols1; - const int kbc0 = (bidx0 + 0)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; - const int kbc0_stop = (bidx0 + 1)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; + const int kbc0 = int64_t(bidx0 + 0)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; + const int kbc0_stop = int64_t(bidx0 + 1)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; const bool did_not_have_any_data = kbc0 == kbc0_stop; const bool wrote_beginning_of_tile = kbc0 % iter_k == 0; @@ -679,7 +679,7 @@ static __global__ void flash_attn_stream_k_fixup( int bidx = bidx0 - 1; int kbc_stop = kbc0; while(true) { - const int kbc = bidx*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; + const int kbc = int64_t(bidx)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; if (kbc == kbc_stop) { // Did not have any data. bidx--; kbc_stop = kbc; diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh index ade0773dad..7bd1044c19 100644 --- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh +++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh @@ -955,22 +955,11 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( (K_h2 + int64_t(kb0)*nbatch_fa*stride_K, tile_K, nbatch_K2, stride_K, k_VKQ_sup); } - for (; kb0 < kb0_stop-1; ++kb0) { - constexpr bool last_iter = false; - constexpr bool oob_check = false; - constexpr int k_VKQ_sup = nbatch_fa; - flash_attn_ext_f16_iter - - (Q_f2, K_h2, V_h2, mask_h, dstk, dstk_fixup, scale, slope, logit_softcap, - ne01, ne02, stride_K, stride_V, stride_mask, tile_Q, tile_K, tile_V, tile_mask, Q_B, VKQ_C, - KQ_max, KQ_rowsum, jt, kb0, k_VKQ_sup); - } // kb0_start is always < kb0_stop so the last iter can be executed unconditionally. if constexpr (ncols2 == 1) { - if (ne11 % nbatch_fa == 0) { - constexpr bool last_iter = true; - constexpr bool oob_check = false; + constexpr bool oob_check = true; + for (; kb0 < kb0_stop-1; ++kb0) { + constexpr bool last_iter = false; constexpr int k_VKQ_sup = nbatch_fa; flash_attn_ext_f16_iter + (Q_f2, K_h2, V_h2, mask_h, dstk, dstk_fixup, scale, slope, logit_softcap, + ne01, ne02, stride_K, stride_V, stride_mask, tile_Q, tile_K, tile_V, tile_mask, Q_B, VKQ_C, + KQ_max, KQ_rowsum, jt, kb0, k_VKQ_sup); + } else { + constexpr bool oob_check = false; + for (; kb0 < kb0_stop-1; ++kb0) { + constexpr bool last_iter = false; + constexpr int k_VKQ_sup = nbatch_fa; flash_attn_ext_f16_iter @@ -989,9 +988,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( ne01, ne02, stride_K, stride_V, stride_mask, tile_Q, tile_K, tile_V, tile_mask, Q_B, VKQ_C, KQ_max, KQ_rowsum, jt, kb0, k_VKQ_sup); } - } else { constexpr bool last_iter = true; - constexpr bool oob_check = false; constexpr int k_VKQ_sup = nbatch_fa; flash_attn_ext_f16_iter ( &Q_tmp[jc*(DKQ/2) + i0/2 + (threadIdx.y % np)*(warp_size*cpy_ne_D/2) + threadIdx.x*(cpy_ne_D/2)], diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu index dec01ff8ad..0155406665 100644 --- a/ggml/src/ggml-cuda/fattn.cu +++ b/ggml/src/ggml-cuda/fattn.cu @@ -36,12 +36,26 @@ static void ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2(ggml_backend_cuda_con const ggml_tensor * KQV = dst; const ggml_tensor * Q = dst->src[0]; const ggml_tensor * K = dst->src[1]; + const ggml_tensor * V = dst->src[2]; const ggml_tensor * mask = dst->src[3]; float max_bias = 0.0f; memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float)); - const bool use_gqa_opt = mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0; + // Edge cases like no mask, ALiBi, unpadded K/V, or misaligned addresses for large data transfers + // are put into the template specialization without GQA optimizations. + bool use_gqa_opt = mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0; + for (const ggml_tensor * t : {Q, K, V, mask}) { + if (t == nullptr) { + continue; + } + for (size_t i = 1; i < GGML_MAX_DIMS; ++i) { + if (t->nb[i] % 16 != 0) { + use_gqa_opt = false; + break; + } + } + } GGML_ASSERT(Q->ne[2] % K->ne[2] == 0); const int gqa_ratio = Q->ne[2] / K->ne[2]; diff --git a/ggml/src/ggml-cuda/fill.cu b/ggml/src/ggml-cuda/fill.cu index eb8ccb7802..739062c405 100644 --- a/ggml/src/ggml-cuda/fill.cu +++ b/ggml/src/ggml-cuda/fill.cu @@ -4,7 +4,7 @@ #define CUDA_FILL_BLOCK_SIZE 256 template -static __global__ void fill_kernel(T * __restrict__ dst, const int64_t k, const T value) { +static __global__ void fill_kernel(T * dst, const int64_t k, const T value) { const int64_t i = (int64_t)blockDim.x * blockIdx.x + threadIdx.x; if (i >= k) { return; diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index d0463388c5..ab0f6fe9ce 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -20,6 +20,7 @@ #include "ggml-cuda/cpy.cuh" #include "ggml-cuda/cross-entropy-loss.cuh" #include "ggml-cuda/diagmask.cuh" +#include "ggml-cuda/diag.cuh" #include "ggml-cuda/fattn.cuh" #include "ggml-cuda/getrows.cuh" #include "ggml-cuda/im2col.cuh" @@ -2641,6 +2642,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: break; + case GGML_OP_DIAG: + ggml_cuda_op_diag(ctx, dst); + break; case GGML_OP_DIAG_MASK_INF: ggml_cuda_op_diag_mask_inf(ctx, dst); break; @@ -4309,6 +4313,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g case GGML_UNARY_OP_EXPM1: case GGML_UNARY_OP_SOFTPLUS: case GGML_UNARY_OP_ELU: + case GGML_UNARY_OP_XIELU: case GGML_UNARY_OP_FLOOR: case GGML_UNARY_OP_CEIL: case GGML_UNARY_OP_ROUND: @@ -4624,9 +4629,10 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_FILL: case GGML_OP_CUMSUM: case GGML_OP_TRI: - return true; + case GGML_OP_DIAG: case GGML_OP_SOLVE_TRI: - return op->src[0]->ne[0] <= 64 && op->src[1]->ne[0] <= 32; + return true; + default: return false; } diff --git a/ggml/src/ggml-cuda/mma.cuh b/ggml/src/ggml-cuda/mma.cuh index 0b13293da9..dcfa40f4d5 100644 --- a/ggml/src/ggml-cuda/mma.cuh +++ b/ggml/src/ggml-cuda/mma.cuh @@ -189,6 +189,9 @@ namespace ggml_cuda_mma { return 8 * (threadIdx.x / 16) + l; #elif defined(RDNA3) return 2 * l + (threadIdx.x / 16); +#else + NO_DEVICE_CODE; + return -1; #endif // defined(RDNA4) } else { NO_DEVICE_CODE; @@ -290,8 +293,12 @@ namespace ggml_cuda_mma { } } #elif defined(AMD_WMMA_AVAILABLE) - +#if defined(RDNA3) + // RDNA3 has duplicated data as input. + static constexpr int ne = I * J / 32 * 2; +#else static constexpr int ne = I * J / 32; +#endif // defined(RDNA3) half2 x[ne] = {{0.0f, 0.0f}}; static constexpr __device__ bool supported() { @@ -310,7 +317,14 @@ namespace ggml_cuda_mma { static __device__ __forceinline__ int get_j(const int l) { if constexpr (I == 16 && J == 8) { +#if defined(RDNA4) return 4 * (threadIdx.x / 16) + l; +#elif defined(RDNA3) + return l; +#else + NO_DEVICE_CODE; + return -1; +#endif // defined(RDNA4) } else { NO_DEVICE_CODE; return -1; @@ -366,11 +380,16 @@ namespace ggml_cuda_mma { static constexpr int I = I_; static constexpr int J = J_; static constexpr data_layout dl = DATA_LAYOUT_I_MAJOR; - static constexpr int ne = I * J / WARP_SIZE; - - nv_bfloat162 x[ne] = {{0.0f, 0.0f}}; #if defined(AMD_WMMA_AVAILABLE) +#if defined(RDNA3) + // RDNA3 has duplicated data as input. + static constexpr int ne = I * J / 32 * 2; +#else + static constexpr int ne = I * J / 32; +#endif // defined(RDNA3) + nv_bfloat162 x[ne] = {{0.0f, 0.0f}}; + static constexpr __device__ bool supported() { if (I == 16 && J == 8) return true; return false; @@ -387,13 +406,23 @@ namespace ggml_cuda_mma { static __device__ __forceinline__ int get_j(const int l) { if constexpr (I == 16 && J == 8) { +#if defined(RDNA4) return 4 * (threadIdx.x / 16) + l; +#elif defined(RDNA3) + return l; +#else + NO_DEVICE_CODE; + return -1; +#endif // defined(RDNA4) } else { NO_DEVICE_CODE; return -1; } } #else + static constexpr int ne = I * J / WARP_SIZE; + nv_bfloat162 x[ne] = {{0.0f, 0.0f}}; + static constexpr __device__ bool supported() { if (I == 8 && J == 8) return true; if (I == 16 && J == 4) return true; @@ -546,8 +575,14 @@ namespace ggml_cuda_mma { } #elif defined(AMD_WMMA_AVAILABLE) if constexpr (std::is_same_v || std::is_same_v) { - ggml_cuda_memcpy_1(t.x, xs0 + t.get_i(0) * stride + t.get_j(0)); - +#if defined(RDNA4) + ggml_cuda_memcpy_1(t.x, xs0 + t.get_i(0) * stride + t.get_j(0)); +#elif defined(RDNA3) + ggml_cuda_memcpy_1(t.x, xs0 + t.get_i(0) * stride + t.get_j(0)); + ggml_cuda_memcpy_1(t.x + t.ne/2, xs0 + t.get_i(0) * stride + t.get_j(t.ne/2)); +#else + NO_DEVICE_CODE; +#endif // defined(RDNA4) } else if constexpr (std::is_same_v) { if constexpr (I == 16 && J == 4) { int64_t * xi = (int64_t *) t.x; @@ -888,6 +923,16 @@ namespace ggml_cuda_mma { const halfx8_t& a_frag = reinterpret_cast(A.x[0]); const halfx8_t& b_frag = reinterpret_cast(B.x[0]); acc_frag = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(a_frag, b_frag, acc_frag); +#elif defined(RDNA3) + using halfx16_t = __attribute__((ext_vector_type(16))) _Float16; + using floatx8_t = __attribute__((ext_vector_type(8))) float; + floatx8_t& acc_frag = reinterpret_cast(D.x[0]); + const halfx16_t& a_frag = reinterpret_cast(A.x[0]); + const halfx16_t& b_frag = reinterpret_cast(B.x[0]); + acc_frag = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32(a_frag, b_frag, acc_frag); +#else + GGML_UNUSED_VARS(D, A, B); + NO_DEVICE_CODE; #endif // RDNA4 #else GGML_UNUSED_VARS(D, A, B); @@ -905,6 +950,16 @@ namespace ggml_cuda_mma { const bf16x8_t& a_frag = reinterpret_cast(A.x[0]); const bf16x8_t& b_frag = reinterpret_cast(B.x[0]); acc_frag = __builtin_amdgcn_wmma_f32_16x16x16_bf16_w32_gfx12(a_frag, b_frag, acc_frag); +#elif defined(RDNA3) + using bf16x16_t = __attribute__((ext_vector_type(16))) __bf16; + using floatx8_t = __attribute__((ext_vector_type(8))) float; + floatx8_t& acc_frag = reinterpret_cast(D.x[0]); + const bf16x16_t& a_frag = reinterpret_cast(A.x[0]); + const bf16x16_t& b_frag = reinterpret_cast(B.x[0]); + acc_frag = __builtin_amdgcn_wmma_f32_16x16x16_bf16_w32(a_frag, b_frag, acc_frag); +#else + GGML_UNUSED_VARS(D, A, B); + NO_DEVICE_CODE; #endif // RDNA4 #else GGML_UNUSED_VARS(D, A, B); diff --git a/ggml/src/ggml-cuda/mmf.cu b/ggml/src/ggml-cuda/mmf.cu index 7cf33f0ddf..6643f243b1 100644 --- a/ggml/src/ggml-cuda/mmf.cu +++ b/ggml/src/ggml-cuda/mmf.cu @@ -151,7 +151,9 @@ bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const return false; } } else { - if (src1_ncols > 16) { + if (GGML_CUDA_CC_IS_RDNA3_0(cc) && src1_ncols > 8) { + return false; + } else if (src1_ncols > 16) { return false; } } @@ -160,9 +162,9 @@ bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const case GGML_TYPE_F32: return ampere_mma_available(cc); case GGML_TYPE_F16: - return volta_mma_available(cc) || turing_mma_available(cc) || (amd_wmma_available(cc) && GGML_CUDA_CC_IS_RDNA4(cc)); + return volta_mma_available(cc) || turing_mma_available(cc) || amd_wmma_available(cc); case GGML_TYPE_BF16: - return ampere_mma_available(cc) || (amd_wmma_available(cc) && GGML_CUDA_CC_IS_RDNA4(cc)); + return ampere_mma_available(cc) || amd_wmma_available(cc); default: return false; } diff --git a/ggml/src/ggml-cuda/mmvf.cu b/ggml/src/ggml-cuda/mmvf.cu index 6238ce7ebd..32948e4d7a 100644 --- a/ggml/src/ggml-cuda/mmvf.cu +++ b/ggml/src/ggml-cuda/mmvf.cu @@ -765,7 +765,10 @@ bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, const int64_t * src0 return ne11 <= 8; } else if (GGML_CUDA_CC_IS_AMD(cc)) { if (fp16_mma_hardware_available(cc)) { - if (GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4(cc)) { + if (GGML_CUDA_CC_IS_RDNA3(cc)) { + return ne11 <= 3; + } + if (GGML_CUDA_CC_IS_RDNA4(cc)) { return ne11 <= 5; } return ne11 <= 2; diff --git a/ggml/src/ggml-cuda/solve_tri.cu b/ggml/src/ggml-cuda/solve_tri.cu index e161d4dc43..177ffc268f 100644 --- a/ggml/src/ggml-cuda/solve_tri.cu +++ b/ggml/src/ggml-cuda/solve_tri.cu @@ -3,6 +3,80 @@ #include "solve_tri.cuh" #define MAX_N_FAST 64 +#define MAX_K_FAST 32 + +static __global__ void get_batch_pointers(const float * A, + float * X, + const float ** A_ptrs, + float ** X_ptrs, + int64_t ne02, + int64_t total_batches, + size_t s02, + size_t s03, + size_t s2, + size_t s3) { + const int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx >= total_batches) { + return; + } + + const int64_t i3 = idx / ne02; + const int64_t i2 = idx % ne02; + + A_ptrs[idx] = A + i3 * s03 + i2 * s02; + X_ptrs[idx] = X + i3 * s3 + i2 * s2; +} + +static void solve_tri_f32_cublas(ggml_backend_cuda_context & ctx, + const float * A, + const float * B, + float * X, + int n, + int k, + int64_t ne02, + int64_t ne03, + size_t s02, + size_t s03, + size_t s12, + size_t s13, + size_t s2, + size_t s3, + cudaStream_t stream) { + const float alpha = 1.0f; + const int64_t total_batches = ne02 * ne03; + if (total_batches == 0) { + return; + } + + // Bulk copy B -> X (contiguous tensors) + if (X != B) { + const int64_t total_elements_BX = n * k * total_batches; + CUDA_CHECK(cudaMemcpyAsync(X, B, total_elements_BX * sizeof(float), cudaMemcpyDeviceToDevice, stream)); + } + + const int id = ggml_cuda_get_device(); + + ggml_cuda_pool_alloc A_ptrs_alloc(ctx.pool(id), total_batches); + ggml_cuda_pool_alloc X_ptrs_alloc(ctx.pool(id), total_batches); + + const float ** A_ptrs_dev = A_ptrs_alloc.get(); + float ** X_ptrs_dev = X_ptrs_alloc.get(); + + get_batch_pointers<<<(total_batches + 255) / 256, 256, 0, stream>>>(A, X, A_ptrs_dev, X_ptrs_dev, ne02, + total_batches, s02, s03, s2, s3); + + CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream)); + + // Yes, this is necessary, without this we get RMSE errors + CUBLAS_CHECK(cublasSetMathMode(ctx.cublas_handle(id), CUBLAS_DEFAULT_MATH)); + CUBLAS_CHECK(cublasStrsmBatched(ctx.cublas_handle(id), CUBLAS_SIDE_RIGHT, CUBLAS_FILL_MODE_UPPER, CUBLAS_OP_N, + CUBLAS_DIAG_NON_UNIT, k, n, &alpha, A_ptrs_dev, n, X_ptrs_dev, k, total_batches)); + + // revert to standard mode from common.cuh + CUBLAS_CHECK(cublasSetMathMode(ctx.cublas_handle(id), CUBLAS_TF32_TENSOR_OP_MATH)); + + GGML_UNUSED_VARS(s12, s13); +} // ====================== // Fast Kernel (n <= 64, k <= 32) - Warp-based parallel reduction @@ -63,7 +137,7 @@ static __global__ void solve_tri_f32_fast(const float * __restrict__ A, float x_low = (lane < n) ? B_batch[lane * k + col_idx] : 0.0f; float x_high = (WARP_SIZE + lane < n) ? B_batch[(WARP_SIZE + lane) * k + col_idx] : 0.0f; - const int half = WARP_SIZE; + const int half = WARP_SIZE; const int nrows_low = (n < half) ? n : half; #pragma unroll @@ -81,8 +155,8 @@ static __global__ void solve_tri_f32_fast(const float * __restrict__ A, #pragma unroll for (int row = half; row < n; ++row) { - float sum = sA[row * n + lane] * x_low; - const int j = half + lane; + float sum = sA[row * n + lane] * x_low; + const int j = half + lane; if (j < row) { sum += sA[row * n + j] * x_high; } @@ -97,7 +171,7 @@ static __global__ void solve_tri_f32_fast(const float * __restrict__ A, for (int rr = 0; rr < 2; ++rr) { const int row = rr * WARP_SIZE + lane; if (row < n) { - const float val = (row < half) ? x_low : x_high; + const float val = (row < half) ? x_low : x_high; X_batch[row * k + col_idx] = val; } } @@ -176,20 +250,26 @@ static void solve_tri_f32_cuda(const float * A, } void ggml_cuda_op_solve_tri(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * src0 = dst->src[0]; // A (triangular n x x matrix) - const ggml_tensor * src1 = dst->src[1]; // B (right hand side of n x k equation columns) + const ggml_tensor * src0 = dst->src[0]; // A (n×n, lower triangular) + const ggml_tensor * src1 = dst->src[1]; // B (n×k) ggml_is_contiguous(src0); ggml_is_contiguous(src1); - const int64_t n = src0->ne[0]; - const int64_t k = src1->ne[0]; + const int64_t n = src0->ne[0]; + const int64_t k = src1->ne[0]; + const int64_t ne02 = src0->ne[2]; + const int64_t ne03 = src0->ne[3]; - GGML_ASSERT(n <= 64); - GGML_ASSERT(k <= 32); - - solve_tri_f32_cuda((const float *) src0->data, (const float *) src1->data, (float *) dst->data, n, k, src0->ne[2], - src0->ne[3], src0->nb[2] / sizeof(float), src0->nb[3] / sizeof(float), - src1->nb[2] / sizeof(float), src1->nb[3] / sizeof(float), dst->nb[2] / sizeof(float), - dst->nb[3] / sizeof(float), ctx.stream()); + if (n <= MAX_N_FAST && k <= MAX_K_FAST) { + solve_tri_f32_cuda((const float *) src0->data, (const float *) src1->data, (float *) dst->data, n, k, + src0->ne[2], src0->ne[3], src0->nb[2] / sizeof(float), src0->nb[3] / sizeof(float), + src1->nb[2] / sizeof(float), src1->nb[3] / sizeof(float), dst->nb[2] / sizeof(float), + dst->nb[3] / sizeof(float), ctx.stream()); + } else { + solve_tri_f32_cublas(ctx, (const float *) src0->data, (const float *) src1->data, (float *) dst->data, n, k, + ne02, ne03, src0->nb[2] / sizeof(float), src0->nb[3] / sizeof(float), + src1->nb[2] / sizeof(float), src1->nb[3] / sizeof(float), dst->nb[2] / sizeof(float), + dst->nb[3] / sizeof(float), ctx.stream()); + } } diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h index b7d6edf7fc..951a88d567 100644 --- a/ggml/src/ggml-cuda/vendors/hip.h +++ b/ggml/src/ggml-cuda/vendors/hip.h @@ -19,6 +19,9 @@ #define CUDA_R_16F HIPBLAS_R_16F #define CUDA_R_16BF HIPBLAS_R_16B #define CUDA_R_32F HIPBLAS_R_32F +#define CUBLAS_SIDE_RIGHT HIPBLAS_SIDE_RIGHT +#define CUBLAS_FILL_MODE_UPPER HIPBLAS_FILL_MODE_UPPER +#define CUBLAS_DIAG_NON_UNIT HIPBLAS_DIAG_NON_UNIT #define CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED hipDeviceAttributeVirtualMemoryManagementSupported #define CU_MEM_ALLOC_GRANULARITY_RECOMMENDED hipMemAllocationGranularityRecommended #define CU_MEM_ALLOCATION_TYPE_PINNED hipMemAllocationTypePinned @@ -30,6 +33,7 @@ #define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width) #define __all_sync(mask, var) __all(var) #define __any_sync(mask, var) __any(var) +#define cublasStrsmBatched hipblasStrsmBatched #define cublasCreate hipblasCreate #define cublasDestroy hipblasDestroy #define cublasGemmEx hipblasGemmEx diff --git a/ggml/src/ggml-cuda/vendors/musa.h b/ggml/src/ggml-cuda/vendors/musa.h index 8c55a2e4e5..221e67f96a 100644 --- a/ggml/src/ggml-cuda/vendors/musa.h +++ b/ggml/src/ggml-cuda/vendors/musa.h @@ -12,11 +12,16 @@ #define CUBLAS_GEMM_DEFAULT_TENSOR_OP MUBLAS_GEMM_DEFAULT #define CUBLAS_OP_N MUBLAS_OP_N #define CUBLAS_OP_T MUBLAS_OP_T +#define CUBLAS_DEFAULT_MATH MUBLAS_DEFAULT_MATH +#define CUBLAS_SIDE_RIGHT MUBLAS_SIDE_RIGHT +#define CUBLAS_FILL_MODE_UPPER MUBLAS_FILL_MODE_UPPER +#define CUBLAS_DIAG_NON_UNIT MUBLAS_DIAG_NON_UNIT #define CUBLAS_STATUS_SUCCESS MUBLAS_STATUS_SUCCESS #define CUBLAS_TF32_TENSOR_OP_MATH MUBLAS_TENSOR_OP_MATH #define CUDA_R_16F MUSA_R_16F #define CUDA_R_16BF MUSA_R_16BF #define CUDA_R_32F MUSA_R_32F +#define cublasStrsmBatched mublasStrsmBatched #define cublasComputeType_t cudaDataType_t #define cublasCreate mublasCreate #define cublasDestroy mublasDestroy diff --git a/ggml/src/ggml-hexagon/ggml-hexagon.cpp b/ggml/src/ggml-hexagon/ggml-hexagon.cpp index 72a82a8911..514f086f68 100644 --- a/ggml/src/ggml-hexagon/ggml-hexagon.cpp +++ b/ggml/src/ggml-hexagon/ggml-hexagon.cpp @@ -1976,9 +1976,6 @@ static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * s break; case GGML_TYPE_F16: - if (!opt_experimental) { - return false; - } break; default: diff --git a/ggml/src/ggml-hexagon/htp/matmul-ops.c b/ggml/src/ggml-hexagon/htp/matmul-ops.c index c99b6a0d18..346f0bd339 100644 --- a/ggml/src/ggml-hexagon/htp/matmul-ops.c +++ b/ggml/src/ggml-hexagon/htp/matmul-ops.c @@ -903,7 +903,7 @@ static void vec_dot_f16_f32(const int n, float * restrict s, const void * restri const float * restrict vy = (const float * restrict) y; for (uint32_t i = 0; i < n; i++) { - rsum += vx[i] * (__fp16) vy[i]; + rsum += (float)vx[i] * vy[i]; } *s = rsum; return; @@ -917,7 +917,7 @@ static void vec_dot_f16_f32(const int n, float * restrict s, const void * restri // for some reason we need volatile here so that the compiler doesn't try anything funky volatile HVX_Vector rsum = Q6_V_vsplat_R(0); - + float r_sum_scalar = 0.0f; uint32_t i = 0; for (i = 0; i < nv0; i++) { @@ -926,31 +926,42 @@ static void vec_dot_f16_f32(const int n, float * restrict s, const void * restri HVX_Vector x = vx[i]; HVX_VectorPair xp = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(x), Q6_Vh_vsplat_R(0x3C00)); // mul by 1.0 - HVX_Vector hi = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_hi_W(xp)), Q6_V_hi_W(yp)); - HVX_Vector lo = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_lo_W(xp)), Q6_V_lo_W(yp)); + //NOTE: need volatile here to prevent compiler optimization + // Seem compiler cannot guarantee read-after-write?? + volatile HVX_Vector hi = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_hi_W(xp)), Q6_V_hi_W(yp)); + volatile HVX_Vector lo = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_lo_W(xp)), Q6_V_lo_W(yp)); HVX_Vector sum = Q6_Vqf32_vadd_Vqf32Vqf32(hi, lo); rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, sum); } if (nv1) { - HVX_VectorPair yp = vy[i]; + // HVX_VectorPair yp = vy[i]; - HVX_Vector x = vx[i]; - HVX_VectorPair xp = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(x), Q6_Vh_vsplat_R(0x3C00)); // mul by 1.0 + // HVX_Vector x = vx[i]; + // HVX_VectorPair xp = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(x), Q6_Vh_vsplat_R(0x3C00)); // mul by 1.0 - if (nv1 >= 32) { - HVX_Vector hi = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_hi_W(xp)), Q6_V_hi_W(yp)); - rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, hi); - nv1 -= 32; - } + // if (nv1 >= 32) { + // volatile HVX_Vector hi = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_hi_W(xp)), Q6_V_hi_W(yp)); + // rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, hi); + // nv1 -= 32; + // } + // rsum = hvx_vec_qf32_reduce_sum(rsum); + + // if (nv1) { + // volatile HVX_Vector lo = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_lo_W(xp)), Q6_V_lo_W(yp)); + // HVX_Vector sum = hvx_vec_qf32_reduce_sum_n(lo, nv1); + // rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, sum); + // } + + //process the remainder using scalar loop rsum = hvx_vec_qf32_reduce_sum(rsum); + const __fp16 * restrict sx = (const __fp16 * restrict) x; + const float * restrict sy = (const float * restrict) y; - if (nv1) { - HVX_Vector lo = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_lo_W(xp)), Q6_V_lo_W(yp)); - HVX_Vector sum = hvx_vec_qf32_reduce_sum_n(lo, nv1); - rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, sum); + for (uint32_t i = nv0 * 64; i < n; i++) { + r_sum_scalar += (float) sx[i] * sy[i]; } // hvx_vec_dump_fp16("X", x); @@ -961,7 +972,7 @@ static void vec_dot_f16_f32(const int n, float * restrict s, const void * restri rsum = hvx_vec_qf32_reduce_sum(rsum); } - *s = hvx_vec_get_fp32(Q6_Vsf_equals_Vqf32(rsum)); + *s = hvx_vec_get_fp32(Q6_Vsf_equals_Vqf32(rsum)) + r_sum_scalar; # ifdef HTP_DEBUG { @@ -1498,9 +1509,6 @@ static void matmul_f16_f32(struct htp_tensor * restrict src0, uint64_t t1, t2; t1 = HAP_perf_get_qtimer_count(); - const size_t src0_row_size = sizeof(__fp16) * ne00; - const size_t src1_row_size = sizeof(float) * ne10; - assert(ne12 % ne02 == 0); assert(ne13 % ne03 == 0); @@ -1510,8 +1518,6 @@ static void matmul_f16_f32(struct htp_tensor * restrict src0, // This is the size of the rest of the dimensions of the result const uint32_t nr1 = ne1 * ne2 * ne3; - uint32_t chunk_size = 64; - // distribute the thread work across the inner or outer loop based on which one is larger uint32_t nchunk0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows uint32_t nchunk1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows @@ -1544,11 +1550,11 @@ static void matmul_f16_f32(struct htp_tensor * restrict src0, const uint32_t blck_0 = 64; const uint32_t blck_1 = 64; - float tmp[32]; + __attribute__((aligned(128))) float tmp[64]; for (uint32_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) { for (uint32_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) { - for (uint32_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir1_end; ir1++) { + for (uint32_t ir1 = iir1; ir1 < MIN(iir1 + blck_1, ir1_end); ir1++) { const uint32_t i13 = (ir1 / (ne12 * ne1)); const uint32_t i12 = (ir1 - i13 * ne12 * ne1) / ne1; const uint32_t i11 = (ir1 - i13 * ne12 * ne1 - i12 * ne1); @@ -1561,13 +1567,16 @@ static void matmul_f16_f32(struct htp_tensor * restrict src0, const uint32_t i2 = i12; const uint32_t i3 = i13; - const uint8_t * restrict src0_row = (const uint8_t *) src0->data + (0 + i02 * nb02 + i03 * nb03); + const uint8_t * restrict src0_base = (const uint8_t *) src0->data + (0 + i02 * nb02 + i03 * nb03); const uint8_t * restrict src1_col = - (const uint8_t *) src1->data + (i11 + i12 * ne11 + i13 * ne12 * ne11) * src1_row_size; + (const uint8_t *) src1->data + (i11 * nb11 + i12 * nb12 + i13 * nb13); float * dst_col = (float *) ((uint8_t * restrict) dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3)); - for (uint32_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ir0++) { - vec_dot_f16_f32(ne00, &tmp[ir0 - iir0], src0_row + ir0 * src0_row_size, src1_col); + const uint32_t ir0_block_end = MIN(iir0 + blck_0, ir0_end); + for (uint32_t ir0 = iir0; ir0 < ir0_block_end; ir0++) { + // Use nb01 stride for non-contiguous src0 support + const uint8_t * restrict src0_row = src0_base + ir0 * nb01; + vec_dot_f16_f32(ne00, &tmp[ir0 - iir0], src0_row, src1_col); } hvx_copy_fp32_ua((uint8_t *) &dst_col[iir0], (uint8_t *) tmp, MIN(iir0 + blck_0, ir0_end) - iir0); diff --git a/ggml/src/ggml-hexagon/htp/rope-ops.c b/ggml/src/ggml-hexagon/htp/rope-ops.c index 00419bcba6..a4399704fc 100644 --- a/ggml/src/ggml-hexagon/htp/rope-ops.c +++ b/ggml/src/ggml-hexagon/htp/rope-ops.c @@ -73,15 +73,15 @@ static float rope_yarn_ramp(const float low, const float high, const int i0) { return (1 - MIN(1, MAX(0, y))); } -static void rope_cache_init(const float theta_base, - float freq_scale, - const float * freq_factors, - float * corr_dims, - uint32_t ne0, - float ext_factor, - float mscale, - float * cache, - float theta_scale) { +static void rope_cache_init(const float theta_base, + const float freq_scale, + const float * freq_factors, + float * corr_dims, + const uint32_t ne0, + const float ext_factor, + const float mscale, + float * cache, + const float theta_scale) { // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py float theta = theta_base; @@ -92,18 +92,19 @@ static void rope_cache_init(const float theta_base, // Get n-d rotational scaling corrected for extrapolation float theta_interp = freq_scale * theta_extrap; - float theta2 = theta_interp; + float theta_final = theta_interp; + float mscale_final = mscale; if (ext_factor != 0.0f) { float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor; - theta2 = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; + theta_final = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; // Get n-d magnitude scaling corrected for interpolation - mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale); + mscale_final *= 1.0f + 0.1f * logf(1.0f / freq_scale); } - cache[i0 + 0] = cosf(theta2) * mscale; - cache[i0 + 1] = sinf(theta2) * mscale; + cache[i0 + 0] = cosf(theta_final) * mscale_final; + cache[i0 + 1] = sinf(theta_final) * mscale_final; theta *= theta_scale; } @@ -151,9 +152,9 @@ static void init_rope_ctx(struct rope_th_ctx * rope_ctx, struct htp_ops_context } static void hvx_calc_rope_neox_f32(const float * restrict src0, - float * restrict dst, - const int num_elems, - const float * restrict theta_cache) { + float * restrict dst, + const int num_elems, + const float * restrict theta_cache) { // for (int i = 0; i < num_elems; i += 2) { //const float cos_theta = theta_cache[i + 0]; //const float sin_theta = theta_cache[i + 1]; @@ -192,7 +193,7 @@ static void hvx_calc_rope_neox_f32(const float * restrict src0, HVX_Vector v4 = Q6_Vqf32_vsub_Vqf32Vqf32(vx0_c, vx1_s); HVX_Vector v5 = Q6_Vqf32_vadd_Vqf32Vqf32(vx0_s, vx1_c); - *(HVX_Vector *) dst_curr = Q6_Vsf_equals_Vqf32(v4); + *(HVX_Vector *) dst_curr = Q6_Vsf_equals_Vqf32(v4); *(HVX_Vector *) (dst_curr + half_size) = Q6_Vsf_equals_Vqf32(v5); src0_curr += VLEN; @@ -259,7 +260,7 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx, const uint32_t ir1, int nth, int ith, - int opt_path) { + const int opt_path) { struct htp_ops_context * octx = rope_ctx->octx; const struct htp_tensor * src0 = &octx->src0; @@ -267,8 +268,8 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx, const struct htp_tensor * src2 = &octx->src2; struct htp_tensor * dst = &octx->dst; - const int32_t mode = rope_ctx->mode; - const bool is_neox = mode & HTP_ROPE_TYPE_NEOX; + const int32_t mode = rope_ctx->mode; + const bool is_neox = mode & HTP_ROPE_TYPE_NEOX; htp_rope_preamble; @@ -281,8 +282,9 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx, freq_factors = (const float *) src2->data; } - int ir = 0; - + const uint32_t i1_end = MIN(ir1, ne1); + const int32_t half_dims = rope_ctx->n_dims / 2; + const size_t remain_bytes = (ne0 - rope_ctx->n_dims) * sizeof(float); for (uint32_t i3 = 0; i3 < ne3; i3++) { // batch for (uint32_t i2 = 0; i2 < ne2; i2++) { // seq-len const int32_t p = pos[i2]; @@ -290,14 +292,7 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx, rope_cache_init(p, rope_ctx->freq_scale, freq_factors, rope_ctx->corr_dims, ne0, rope_ctx->ext_factor, rope_ctx->attn_factor, wp0, rope_ctx->theta_scale); - for (uint32_t i1 = 0; i1 < ne1; i1++) { // attn-heads - if (ir++ < ir0) { - continue; - } - if (ir > ir1) { - break; - } - + for (uint32_t i1 = ir0; i1 < i1_end; i1++) { // attn-heads const float * src = (float *) ((char *) src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01); float * dst_data = (float *) ((char *) dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1); @@ -310,6 +305,9 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx, } else { hvx_calc_rope_f32(src_loc, dst_data_loc, rope_ctx->n_dims, wp0); } + + src_loc += rope_ctx->n_dims; + dst_data_loc += rope_ctx->n_dims; } else { for (uint32_t i0 = 0; i0 < rope_ctx->n_dims; i0 += 2) { const float cos_theta = wp0[i0 + 0]; @@ -317,10 +315,10 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx, if (is_neox) { const float x0 = src_loc[0]; - const float x1 = src_loc[rope_ctx->n_dims/2]; + const float x1 = src_loc[half_dims]; - dst_data_loc[0] = x0 * cos_theta - x1 * sin_theta; - dst_data_loc[rope_ctx->n_dims/2] = x0 * sin_theta + x1 * cos_theta; + dst_data_loc[0] = x0 * cos_theta - x1 * sin_theta; + dst_data_loc[half_dims] = x0 * sin_theta + x1 * cos_theta; src_loc += 1; dst_data_loc += 1; @@ -335,15 +333,13 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx, dst_data_loc += 2; } } + + src_loc += (is_neox ? half_dims : 0); + dst_data_loc += (is_neox ? half_dims : 0); } - for (uint32_t i0 = rope_ctx->n_dims; i0 < ne0; i0 += 2) { - dst_data_loc[0] = src_loc[0]; - dst_data_loc[1] = src_loc[1]; - - src_loc += 2; - dst_data_loc += 2; - } + // TODO: use simd to speed up the remaining elements copy + memcpy(dst_data_loc, src_loc, remain_bytes); } } } diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp index ba3c342751..680904d132 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.cpp +++ b/ggml/src/ggml-metal/ggml-metal-device.cpp @@ -411,6 +411,38 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_conv(ggml_me return res; } +ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_conv_batched(ggml_metal_library_t lib, const ggml_tensor * op, int ssm_conv_bs) { + GGML_ASSERT(op->src[0]->type == GGML_TYPE_F32); + GGML_ASSERT(op->src[1]->type == GGML_TYPE_F32); + + GGML_ASSERT(ggml_is_contiguous(op->src[0])); + GGML_ASSERT(ggml_is_contiguous(op->src[1])); + + char base[256]; + char name[256]; + + const char * suffix = ""; + if (op->src[1]->ne[0] % 4 == 0) { + suffix = "_4"; + } + + snprintf(base, 256, "kernel_ssm_conv_%s_%s_batched%s", ggml_type_name(op->src[0]->type), ggml_type_name(op->src[1]->type), suffix); + snprintf(name, 256, "%s_ssm_conv_bs=%d", base, ssm_conv_bs); + + ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name); + if (!res.pipeline) { + ggml_metal_cv_t cv = ggml_metal_cv_init(); + + ggml_metal_cv_set_int16(cv, ssm_conv_bs, FC_SSM_CONV + 0); + + res = ggml_metal_library_compile_pipeline(lib, base, name, cv); + + ggml_metal_cv_free(cv); + } + + return res; +} + ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_scan(ggml_metal_library_t lib, const ggml_tensor * op) { GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne); @@ -427,7 +459,12 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_scan(ggml_me res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr); } - res.smem = 32*sizeof(float)*nsg; + // Shared memory layout: + // - sgptg * NW floats for partial sums (nsg * 32) + // - sgptg floats for shared_x_dt (nsg) + // - sgptg floats for shared_dA (nsg) + // Total: nsg * (32 + 2) floats + res.smem = (32 + 2)*sizeof(float)*nsg; return res; } diff --git a/ggml/src/ggml-metal/ggml-metal-device.h b/ggml/src/ggml-metal/ggml-metal-device.h index 77f2e98cfe..0a8b9211a7 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.h +++ b/ggml/src/ggml-metal/ggml-metal-device.h @@ -117,6 +117,7 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_cumsum_ad struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_tri (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_soft_max (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_conv (ggml_metal_library_t lib, const struct ggml_tensor * op); +struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_conv_batched (ggml_metal_library_t lib, const struct ggml_tensor * op, int ssm_conv_bs); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_scan (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_rwkv (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv_ext (ggml_metal_library_t lib, enum ggml_type tsrc0, enum ggml_type tsrc1, int nsg, int nxpsg, int r1ptg); diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m index 7b7d1c1233..f24270bb1c 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.m +++ b/ggml/src/ggml-metal/ggml-metal-device.m @@ -769,9 +769,16 @@ ggml_metal_device_t ggml_metal_device_init(void) { #endif dev->props.use_shared_buffers = dev->props.has_unified_memory; +#if TARGET_OS_OSX + // In case of eGPU, shared memory may be preferable. + dev->props.use_shared_buffers |= [dev->mtl_device location] == MTLDeviceLocationExternal; +#endif if (getenv("GGML_METAL_SHARED_BUFFERS_DISABLE") != NULL) { dev->props.use_shared_buffers = false; } + if (getenv("GGML_METAL_SHARED_BUFFERS_ENABLE") != NULL) { + dev->props.use_shared_buffers = true; + } dev->props.supports_gpu_family_apple7 = [dev->mtl_device supportsFamily:MTLGPUFamilyApple7]; diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h index 30109f83e1..8944b07e90 100644 --- a/ggml/src/ggml-metal/ggml-metal-impl.h +++ b/ggml/src/ggml-metal/ggml-metal-impl.h @@ -77,6 +77,7 @@ #define FC_MUL_MV 600 #define FC_MUL_MM 700 #define FC_ROPE 800 +#define FC_SSM_CONV 900 // op-specific constants #define OP_FLASH_ATTN_EXT_NQPTG 8 diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp index 9efd51abba..e99c1763f6 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.cpp +++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp @@ -221,7 +221,7 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) { } if (ctx->debug_graph > 0) { - GGML_LOG_DEBUG("%s: node[%5d] - %-12s %s\n", __func__, idx, ggml_op_name(node->op), is_concurrent ? "(concurrent)" : ""); + GGML_LOG_DEBUG("%s: node[%5d] - %-12s %-12s %s\n", __func__, idx, ggml_op_name(node->op), ggml_get_name(node), is_concurrent ? "(concurrent)" : ""); } if (ctx->debug_graph > 1) { GGML_TENSOR_LOCALS( int64_t, ne0, node->src[0], ne); @@ -1365,15 +1365,43 @@ int ggml_metal_op_ssm_conv(ggml_metal_op_t ctx, int idx) { /*.nb2 =*/ nb2, }; - auto pipeline = ggml_metal_library_get_pipeline_ssm_conv(lib, op); + // Use batched kernel for prefill (ne1 > 1) to reduce threadgroup dispatch overhead + const bool use_batched = (ne1 > 1); - ggml_metal_encoder_set_pipeline(enc, pipeline); - ggml_metal_encoder_set_bytes(enc, &args, sizeof(args), 0); - ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[0]), 1); - ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[1]), 2); - ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op), 3); + if (use_batched) { + // Determine the smallest power of 2 that's >= ne1, but <= 256 + int BATCH_SIZE; + if (ne1 > 128) BATCH_SIZE = 256; + else if (ne1 > 64 ) BATCH_SIZE = 128; + else if (ne1 > 32 ) BATCH_SIZE = 64; + else if (ne1 > 16 ) BATCH_SIZE = 32; + else if (ne1 > 8 ) BATCH_SIZE = 16; + else if (ne1 > 4 ) BATCH_SIZE = 8; + else BATCH_SIZE = 2; - ggml_metal_encoder_dispatch_threadgroups(enc, ne01, ne1, ne02, 1, 1, 1); + auto pipeline = ggml_metal_library_get_pipeline_ssm_conv_batched(lib, op, BATCH_SIZE); + + ggml_metal_encoder_set_pipeline(enc, pipeline); + ggml_metal_encoder_set_bytes(enc, &args, sizeof(args), 0); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[0]), 1); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[1]), 2); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op), 3); + + // Dispatch: ne01 rows, ceil(ne1/BATCH_SIZE) token batches, ne02 sequences + // Each threadgroup has BATCH_SIZE threads, each handling one token + const int n_token_batches = (ne1 + BATCH_SIZE - 1) / BATCH_SIZE; + ggml_metal_encoder_dispatch_threadgroups(enc, ne01, n_token_batches, ne02, BATCH_SIZE, 1, 1); + } else { + auto pipeline = ggml_metal_library_get_pipeline_ssm_conv(lib, op); + + ggml_metal_encoder_set_pipeline(enc, pipeline); + ggml_metal_encoder_set_bytes(enc, &args, sizeof(args), 0); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[0]), 1); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[1]), 2); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op), 3); + + ggml_metal_encoder_dispatch_threadgroups(enc, ne01, ne1, ne02, 1, 1, 1); + } return 1; } diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal index 4b78d5a2ba..51bcbae309 100644 --- a/ggml/src/ggml-metal/ggml-metal.metal +++ b/ggml/src/ggml-metal/ggml-metal.metal @@ -2343,7 +2343,102 @@ kernel void kernel_ssm_conv_f32_f32_4( x[0] = sumf; } +constant short FC_ssm_conv_bs [[function_constant(FC_SSM_CONV + 0)]]; + +// Batched version: each threadgroup processes multiple tokens for better efficiency +// Thread layout: each thread handles one token, threadgroup covers BATCH_SIZE tokens +kernel void kernel_ssm_conv_f32_f32_batched( + constant ggml_metal_kargs_ssm_conv & args, + device const void * src0, + device const void * src1, + device float * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + // tgpig.x = row index (ir) + // tgpig.y = batch of tokens (i2_base / BATCH_SIZE) + // tgpig.z = sequence index (i3) + // tpitg.x = thread within batch (0..BATCH_SIZE-1) + const short BATCH_SIZE = FC_ssm_conv_bs; + + const int64_t ir = tgpig.x; + const int64_t i2_base = tgpig.y * BATCH_SIZE; + const int64_t i3 = tgpig.z; + const int64_t i2_off = tpitg.x; + const int64_t i2 = i2_base + i2_off; + + const int64_t nc = args.ne10; // conv kernel size (typically 4) + const int64_t n_t = args.ne1; // number of tokens + + // Bounds check for partial batches at the end + if (i2 >= n_t) { + return; + } + + // Load conv weights (shared across all tokens for this row) + device const float * c = (device const float *) ((device const char *) src1 + ir*args.nb11); + + // Load source for this specific token + device const float * s = (device const float *) ((device const char *) src0 + ir*args.nb01 + i2*args.nb00 + i3*args.nb02); + + // Output location for this token + device float * x = (device float *) ((device char *) dst + ir*args.nb0 + i2*args.nb1 + i3*args.nb2); + + float sumf = 0.0f; + for (int64_t i0 = 0; i0 < nc; ++i0) { + sumf += s[i0] * c[i0]; + } + + x[0] = sumf; +} + +kernel void kernel_ssm_conv_f32_f32_batched_4( + constant ggml_metal_kargs_ssm_conv & args, + device const void * src0, + device const void * src1, + device float * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + // tgpig.x = row index (ir) + // tgpig.y = batch of tokens (i2_base / BATCH_SIZE) + // tgpig.z = sequence index (i3) + // tpitg.x = thread within batch (0..BATCH_SIZE-1) + const short BATCH_SIZE = FC_ssm_conv_bs; + + const int64_t ir = tgpig.x; + const int64_t i2_base = tgpig.y * BATCH_SIZE; + const int64_t i3 = tgpig.z; + const int64_t i2_off = tpitg.x; + const int64_t i2 = i2_base + i2_off; + + const int64_t nc = args.ne10; // conv kernel size (typically 4) + const int64_t n_t = args.ne1; // number of tokens + + // Bounds check for partial batches at the end + if (i2 >= n_t) { + return; + } + + // Load conv weights (shared across all tokens for this row) + device const float4 * c = (device const float4 *) ((device const char *) src1 + ir*args.nb11); + + // Load source for this specific token + device const float4 * s = (device const float4 *) ((device const char *) src0 + ir*args.nb01 + i2*args.nb00 + i3*args.nb02); + + // Output location for this token + device float * x = (device float *) ((device char *) dst + ir*args.nb0 + i2*args.nb1 + i3*args.nb2); + + float sumf = 0.0f; + for (int64_t i0 = 0; i0 < nc/4; ++i0) { + sumf += dot(s[i0], c[i0]); + } + + x[0] = sumf; +} + // ref: ggml.c:ggml_compute_forward_ssm_scan_f32, Mamba-2 part +// Optimized version: reduces redundant memory loads by having one thread load shared values kernel void kernel_ssm_scan_f32( constant ggml_metal_kargs_ssm_scan & args, device const void * src0, @@ -2363,7 +2458,15 @@ kernel void kernel_ssm_scan_f32( uint3 tgpg[[threadgroups_per_grid]]) { constexpr short NW = N_SIMDWIDTH; - shared[tpitg.x] = 0.0f; + // Shared memory layout: + // [0..sgptg*NW-1]: partial sums for reduction (existing) + // [sgptg*NW..sgptg*NW+sgptg-1]: pre-computed x_dt values for each token in batch + // [sgptg*NW+sgptg..sgptg*NW+2*sgptg-1]: pre-computed dA values for each token in batch + threadgroup float * shared_sums = shared; + threadgroup float * shared_x_dt = shared + sgptg * NW; + threadgroup float * shared_dA = shared + sgptg * NW + sgptg; + + shared_sums[tpitg.x] = 0.0f; const int32_t i0 = tpitg.x; const int32_t i1 = tgpig.x; @@ -2403,32 +2506,47 @@ kernel void kernel_ssm_scan_f32( for (int i2 = 0; i2 < n_t; i2 += sgptg) { threadgroup_barrier(mem_flags::mem_threadgroup); - for (int t = 0; t < sgptg && i2 + t < n_t; t++) { - const float dt0 = dt[0]; + // Pre-compute x_dt and dA for this batch of tokens + // Only first sgptg threads do the loads and expensive math + if (i0 < sgptg && i2 + i0 < n_t) { + // ns12 and ns21 are element strides (nb12/nb10, nb21/nb20) + device const float * x_t = x + i0 * args.ns12; + device const float * dt_t = dt + i0 * args.ns21; + + const float dt0 = dt_t[0]; const float dtsp = dt0 <= 20.0f ? log(1.0f + exp(dt0)) : dt0; - const float x_dt = x[0] * dtsp; - const float dA = exp(dtsp * A0); + shared_x_dt[i0] = x_t[0] * dtsp; + shared_dA[i0] = dtsp; // Store dtsp, compute exp(dtsp * A0) per-thread since A0 varies + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + for (int t = 0; t < sgptg && i2 + t < n_t; t++) { + const float x_dt = shared_x_dt[t]; + const float dA = exp(shared_dA[t] * A0); s = (s0 * dA) + (B[i0] * x_dt); const float sumf = simd_sum(s * C[i0]); if (tiisg == 0) { - shared[t*NW + sgitg] = sumf; + shared_sums[t*NW + sgitg] = sumf; } // recurse s0 = s; - x += args.ns12; - dt += args.ns21; B += args.ns42; C += args.ns52; } + // Advance pointers for next batch + x += sgptg * args.ns12; + dt += sgptg * args.ns21; + threadgroup_barrier(mem_flags::mem_threadgroup); - const float sumf = simd_sum(shared[sgitg*NW + tiisg]); + const float sumf = simd_sum(shared_sums[sgitg*NW + tiisg]); if (tiisg == 0 && i2 + sgitg < n_t) { y[sgitg*nh*nr] = sumf; diff --git a/ggml/src/ggml-sycl/add-id.cpp b/ggml/src/ggml-sycl/add-id.cpp new file mode 100644 index 0000000000..00c073cf93 --- /dev/null +++ b/ggml/src/ggml-sycl/add-id.cpp @@ -0,0 +1,77 @@ +#include +#include "common.hpp" +#include "add-id.hpp" + +static void add_id_kernel( + const float* src0, + const float* src1, + const int32_t* src2, + float* dst, + int64_t ne0, + int64_t ne1, + size_t nb01, + size_t nb02, + size_t nb11, + size_t nb21, + sycl::nd_item<3> item_ct1) { + const int64_t i1 = item_ct1.get_group(2); + const int64_t i2 = item_ct1.get_group(1); + + const int i11 = + *(const int32_t*)((const char*)src2 + i1 * sizeof(int32_t) + i2 * nb21); + + const size_t nb1 = ne0 * sizeof(float); + const size_t nb2 = ne1 * nb1; + + float* dst_row = (float*)((char*)dst + i1 * nb1 + i2 * nb2); + const float* src0_row = + (const float*)((const char*)src0 + i1 * nb01 + i2 * nb02); + const float* src1_row = (const float*)((const char*)src1 + i11 * nb11); + + for (int64_t i0 = item_ct1.get_local_id(2); i0 < ne0; + i0 += item_ct1.get_local_range(2)) { + dst_row[i0] = src0_row[i0] + src1_row[i0]; + } +} + +void ggml_sycl_add_id(ggml_backend_sycl_context& ctx, ggml_tensor* dst) { + const ggml_tensor* src0 = dst->src[0]; + const ggml_tensor* src1 = dst->src[1]; + const ggml_tensor* src2 = dst->src[2]; + + GGML_TENSOR_TERNARY_OP_LOCALS + + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(src2->type == GGML_TYPE_I32); + + GGML_ASSERT(nb00 == sizeof(float)); + GGML_ASSERT(nb10 == sizeof(float)); + GGML_ASSERT(nb20 == sizeof(int32_t)); + + const float* src0_d = (const float*)src0->data; + const float* src1_d = (const float*)src1->data; + const int32_t* src2_d = (const int32_t*)src2->data; + float* dst_d = (float*)dst->data; + + int threads = std::min((int)ne00, 768); // cols + ctx.stream()->parallel_for( + sycl::nd_range<3>( + sycl::range<3>(1, ne02, ne01) * sycl::range<3>(1, 1, threads), + sycl::range<3>(1, 1, threads)), + [=](sycl::nd_item<3> item_ct1) { + add_id_kernel( + src0_d, + src1_d, + src2_d, + dst_d, + ne0, + ne1, + nb01, + nb02, + nb11, + nb21, + item_ct1); + }); +} diff --git a/ggml/src/ggml-sycl/add-id.hpp b/ggml/src/ggml-sycl/add-id.hpp new file mode 100644 index 0000000000..e1b09ee8c7 --- /dev/null +++ b/ggml/src/ggml-sycl/add-id.hpp @@ -0,0 +1,8 @@ +#ifndef GGML_SYCL_ADD_ID_HPP +#define GGML_SYCL_ADD_ID_HPP + +#include "common.hpp" + +void ggml_sycl_add_id(ggml_backend_sycl_context & ctx, ggml_tensor * dst); + +#endif // GGML_SYCL_ADD_ID_HPP diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp index 637630c1d2..519638fd41 100644 --- a/ggml/src/ggml-sycl/common.hpp +++ b/ggml/src/ggml-sycl/common.hpp @@ -642,5 +642,22 @@ static __dpct_inline__ sycl::uint2 fast_div_modulo(uint32_t n, const sycl::uint3 return sycl::uint2(div_val, mod_val); } +static __dpct_inline__ int ggml_sycl_dp4a(const int a, const int b, int c) { + return dpct::dp4a(a, b, c); +} + +static __dpct_inline__ float ggml_sycl_e8m0_to_fp32(uint8_t x) { + uint32_t bits; + if (x == 0) { + bits = 0x00400000; + } else { + bits = (uint32_t) x << 23; + } + + float result; + memcpy(&result, &bits, sizeof(float)); + return result; +} + #endif // GGML_SYCL_COMMON_HPP diff --git a/ggml/src/ggml-sycl/convert.cpp b/ggml/src/ggml-sycl/convert.cpp index 7c6ea8a57a..8bdae36458 100644 --- a/ggml/src/ggml-sycl/convert.cpp +++ b/ggml/src/ggml-sycl/convert.cpp @@ -472,6 +472,16 @@ static void dequantize_row_iq4_nl_sycl(const void *vx, dst_t *y, const int64_t k } } +template +static void dequantize_row_mxfp4_sycl(const void * vx, dst_t * y, const int64_t k, dpct::queue_ptr stream) { + const int nb = (k + QK_K - 1) / QK_K; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_mxfp4(vx, y, item_ct1); + }); +} + template static void convert_unary_nc(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t s01, const int64_t s02, const int64_t s03, @@ -518,6 +528,7 @@ static void convert_unary_sycl(const void * vx, dst_t * y, const int64_t k, dpct convert_unary_nc_sycl(vx, y, k, 1, 1, 1, k, k, k, queue); } + to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) { switch (type) { case GGML_TYPE_Q4_0: @@ -571,6 +582,8 @@ to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) { return dequantize_row_iq4_xs_sycl; case GGML_TYPE_IQ4_NL: return dequantize_row_iq4_nl_sycl; + case GGML_TYPE_MXFP4: + return dequantize_row_mxfp4_sycl; case GGML_TYPE_F32: return convert_unary_sycl; #ifdef GGML_SYCL_HAS_BF16 @@ -636,6 +649,8 @@ to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor *dst) { return dequantize_row_iq4_xs_sycl; case GGML_TYPE_IQ4_NL: return dequantize_row_iq4_nl_sycl; + case GGML_TYPE_MXFP4: + return dequantize_row_mxfp4_sycl; case GGML_TYPE_F16: return convert_unary_sycl; #ifdef GGML_SYCL_HAS_BF16 diff --git a/ggml/src/ggml-sycl/dequantize.hpp b/ggml/src/ggml-sycl/dequantize.hpp index 540539bb22..da2a605daa 100644 --- a/ggml/src/ggml-sycl/dequantize.hpp +++ b/ggml/src/ggml-sycl/dequantize.hpp @@ -819,5 +819,23 @@ dequantize_block_iq4_xs(const void *__restrict__ vx, dst_t *__restrict__ yy, } } +template +static void dequantize_block_mxfp4(const void * __restrict__ vx, dst_t * __restrict__ yy, + const sycl::nd_item<3> &item_ct1) { + // auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const int64_t i = item_ct1.get_group(2); + const block_mxfp4 * x = (const block_mxfp4 *) vx + i*(QK_K/QK_MXFP4); + + const int64_t tid = item_ct1.get_local_id(2); + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 4*il; + const uint8_t * q4 = x[ib].qs + 4*il; + const float d = ggml_sycl_e8m0_to_fp32(x[ib].e); + for (int j = 0; j < 4; ++j) { + y[j+ 0] = d * kvalues_mxfp4[q4[j] & 0xf]*0.5f; + y[j+16] = d * kvalues_mxfp4[q4[j] >> 4]*0.5f; + } +} #endif // GGML_SYCL_DEQUANTIZE_HPP diff --git a/ggml/src/ggml-sycl/dpct/helper.hpp b/ggml/src/ggml-sycl/dpct/helper.hpp index f93cfa701f..30ec1e8daf 100644 --- a/ggml/src/ggml-sycl/dpct/helper.hpp +++ b/ggml/src/ggml-sycl/dpct/helper.hpp @@ -1860,10 +1860,31 @@ namespace dpct : id); } + template + using dot_product_acc_t = std::conditional_t< + std::is_unsigned_v && std::is_unsigned_v, + uint32_t, + int32_t>; + + template + sycl::vec extract_and_sign_or_zero_extend4(T val) { + return sycl::vec(val) + .template as, int8_t, uint8_t>, + 4>>() + .template convert(); + } + template - inline auto dp4a(T1 a, T2 b, T3 c) - { - return syclcompat::dp4a(a, b, c); + inline auto dp4a(T1 a, T2 b, T3 c) { + dot_product_acc_t res = c; + auto va = extract_and_sign_or_zero_extend4(a); + auto vb = extract_and_sign_or_zero_extend4(b); + res += va[0] * vb[0]; + res += va[1] * vb[1]; + res += va[2] * vb[2]; + res += va[3] * vb[3]; + return res; } struct sub_sat @@ -2972,6 +2993,38 @@ namespace dpct atomic_fetch_add(addr, operand, memoryOrder); } + inline unsigned int byte_level_permute( + unsigned int a, unsigned int b, unsigned int s) { + unsigned int ret; + ret = ((((std::uint64_t)b << 32 | a) >> (s & 0x7) * 8) & 0xff) | + (((((std::uint64_t)b << 32 | a) >> ((s >> 4) & 0x7) * 8) & 0xff) + << 8) | + (((((std::uint64_t)b << 32 | a) >> ((s >> 8) & 0x7) * 8) & 0xff) + << 16) | + (((((std::uint64_t)b << 32 | a) >> ((s >> 12) & 0x7) * 8) & 0xff) + << 24); + return ret; + } + + inline uint32_t byte_level_permute_custom( + uint32_t low32, uint32_t high32, uint32_t sel, int mode = 0) { + constexpr uint16_t lookup[6][4] = { + {0x3210, 0x4321, 0x5432, 0x6543}, // Forward 4-byte extract + {0x5670, 0x6701, 0x7012, 0x0123}, // Backward 4-byte extract + {0x0000, 0x1111, 0x2222, 0x3333}, // Replicate 8-bit values + {0x3210, 0x3211, 0x3222, 0x3333}, // Edge clamp left + {0x0000, 0x1110, 0x2210, 0x3210}, // Edge clamp right + {0x1010, 0x3232, 0x1010, 0x3232} // Replicate 16-bit values + }; + + if (mode >= 1 && mode <= 6) { + return byte_level_permute(low32, high32, lookup[mode - 1][sel & 0x3]); + } else if (!mode) { + return byte_level_permute(low32, high32, sel); + } + return 0; + } + } // COPY from DPCT head files #endif // GGML_SYCL_DPCT_HELPER_HPP diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp index 7d54ce600e..8d83b2446b 100644 --- a/ggml/src/ggml-sycl/element_wise.cpp +++ b/ggml/src/ggml-sycl/element_wise.cpp @@ -911,6 +911,98 @@ static inline void ggml_sycl_op_swiglu(ggml_backend_sycl_context & ctx, ggml_ten }); } +__dpct_inline__ float ggml_sycl_op_swiglu_oai_single(float x, float g, float alpha = 1.702f, float limit = 7.0f) { + x = sycl::fmin(x, limit); + g = sycl::fmax(sycl::fmin(g, limit), -limit); + + float out_glu = x / (1.0f + sycl::native::exp(-x * alpha)); + out_glu = out_glu * (1.0f + g); + return out_glu; +} + + +template +static void swiglu_oai_kernel(const T * x, const T * g, T * dst, const int64_t k, + const int64_t n, const int64_t o0, const int64_t o1, + float alpha, float limit, sycl::nd_item<3> item_ct1) { + const int64_t i = int64_t(item_ct1.get_local_range(2)) * item_ct1.get_group(2) + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + + const int64_t j0 = (i / n) * o0 + (i % n); + const int64_t j1 = o0 == o1 ? j0 : (i / n) * o1 + (i % n); + + float xi = x[j0]; + float gi = g[j1]; + + dst[i] = ggml_sycl_op_swiglu_oai_single(xi, gi, alpha, limit); +} + +template +static void swiglu_oai_sycl(const T * x, + const T * g, + T * dst, + const int64_t k, + const int64_t n, + const int64_t o0, + const int64_t o1, + const float alpha, + const float limit, + dpct::queue_ptr stream) { + const int64_t num_blocks = (k + SYCL_GLU_BLOCK_SIZE - 1) / SYCL_GLU_BLOCK_SIZE; + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_GLU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_GLU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + swiglu_oai_kernel(x, g, dst, k, n, o0, o1, alpha, limit, item_ct1); + }); +} + +void ggml_sycl_op_swiglu_oai(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + void * src0_d = src0->data; + void * src1_d = src1 ? src1->data : src0->data; + const int64_t src0_o = src0->nb[1]; + const int64_t src1_o = src1 ? src1->nb[1] : src0->nb[1]; + void * dst_d = dst->data; + const int64_t nc = src1 ? src0->ne[0] : src0->ne[0] / 2; + dpct::queue_ptr stream = ctx.stream(); + + GGML_ASSERT(ggml_is_contiguous_1(src0)); + GGML_ASSERT(src0->nb[0] == ggml_element_size(src0)); + GGML_ASSERT(ggml_is_contiguous(dst)); + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + GGML_ASSERT(src0->type == dst->type); + GGML_ASSERT(dst->ne[0] == nc); + GGML_ASSERT(ggml_nrows(dst) == ggml_nrows(src0)); + + if (src1) { + GGML_ASSERT(ggml_is_contiguous_1(src1)); + GGML_ASSERT(src1->nb[0] == ggml_element_size(src1)); + GGML_ASSERT(src1->ne[0] == nc); + GGML_ASSERT(src0->type == src1->type); + } + + //const int32_t swapped = ((const int32_t *) dst->op_params)[1]; + const int32_t swapped = ggml_get_op_params_i32(dst, 1); + const float alpha = ggml_get_op_params_f32(dst, 2); + const float limit = ggml_get_op_params_f32(dst, 3); + + float * src0_p = (float *) src0_d; + float * src1_p = (float *) src1_d; + + if (!src1) { + src0_p += swapped ? nc : 0; + src1_p += swapped ? 0 : nc; + } + + swiglu_oai_sycl(src0_p, src1_p, (float *)dst_d, ggml_nelements(dst), nc, src0_o / sizeof(float), src1_o / sizeof(float), alpha, limit, stream); +} + static inline void ggml_sycl_op_geglu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst, [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) { @@ -1070,6 +1162,11 @@ void ggml_sycl_swiglu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { ggml_sycl_op_swiglu(ctx, dst); } +void ggml_sycl_swiglu_oai(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); + ggml_sycl_op_swiglu_oai(ctx, dst); +} + void ggml_sycl_geglu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); ggml_sycl_op_geglu_erf(ctx, dst); diff --git a/ggml/src/ggml-sycl/element_wise.hpp b/ggml/src/ggml-sycl/element_wise.hpp index fcf93295cb..0913a2e529 100644 --- a/ggml/src/ggml-sycl/element_wise.hpp +++ b/ggml/src/ggml-sycl/element_wise.hpp @@ -5,6 +5,8 @@ #include "ggml.h" #include // For std::numeric_limits +#define SYCL_GLU_BLOCK_SIZE 256 + template T neg_infinity() { return -std::numeric_limits::infinity(); @@ -41,6 +43,8 @@ void ggml_sycl_silu(ggml_backend_sycl_context & ctx, ggml_tensor * dst); void ggml_sycl_gelu_quick(ggml_backend_sycl_context & ctx, ggml_tensor * dst); +void ggml_sycl_swiglu_oai(ggml_backend_sycl_context & ctx, ggml_tensor * dst); + void ggml_sycl_gelu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst); void ggml_sycl_tanh(ggml_backend_sycl_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index 7449a91609..e996d98be8 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -39,6 +39,7 @@ #include "ggml-impl.h" #include "ggml-backend-impl.h" +#include "ggml-sycl/add-id.hpp" #include "ggml-sycl/backend.hpp" #include "ggml-sycl/common.hpp" #include "ggml-sycl/element_wise.hpp" @@ -3313,6 +3314,7 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor bool use_mul_mat_q = ggml_sycl_supports_mmq(src0->type) && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32; + // mmvq and mmq need the __dp4a instruction which is available for gen12+ // Workaround in https://github.com/ggerganov/llama.cpp/commit/95f84d5ce8b449a9b16009434aca800df504a02e use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS); @@ -3320,7 +3322,6 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor use_mul_mat_q = use_mul_mat_q && (src1->ne[1] <= MMQ_MAX_BATCH_SIZE); #endif // SYCL_USE_XMX - // mmvq path is faster in the CUDA backend. if (!g_ggml_sycl_prioritize_dmmv && (ctx.stream()->get_backend() == sycl::backend::ext_oneapi_cuda // Dispatch becomes obscure with the reorder, MMVQ when the reorder optimization @@ -3711,6 +3712,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg case GGML_OP_ADD1: // TODO: more efficient implementation ggml_sycl_add(ctx, dst); break; + case GGML_OP_ADD_ID: + ggml_sycl_add_id(ctx, dst); + break; case GGML_OP_SUB: ggml_sycl_sub(ctx, dst); break; @@ -3803,6 +3807,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg case GGML_GLU_OP_SWIGLU: ggml_sycl_swiglu(ctx, dst); break; + case GGML_GLU_OP_SWIGLU_OAI: + ggml_sycl_swiglu_oai(ctx, dst); + break; case GGML_GLU_OP_GEGLU_ERF: ggml_sycl_geglu_erf(ctx, dst); break; @@ -4397,6 +4404,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g case GGML_GLU_OP_REGLU: case GGML_GLU_OP_GEGLU: case GGML_GLU_OP_SWIGLU: + case GGML_GLU_OP_SWIGLU_OAI: case GGML_GLU_OP_GEGLU_ERF: case GGML_GLU_OP_GEGLU_QUICK: return ggml_is_contiguous_1(op->src[0]); @@ -4424,15 +4432,18 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g } } ggml_type src0_type = op->src[0]->type; - if (src0_type == GGML_TYPE_BF16 || src0_type == GGML_TYPE_MXFP4) { - // TODO: support MXFP4 + if (src0_type == GGML_TYPE_BF16 ) { + // TODO: support GGML_TYPE_BF16 // FIXME: keep a list of supported types to avoid breaking the backend when a new type is added return false; } + // TODO: The configuration below needs more work to be supported with oneDNN - if (ggml_is_permuted(a) && !ggml_is_contiguous(a) && a->ne[2] > 1 && a->ne[3] > 1) { - return false; + if (ggml_is_permuted(a) && !ggml_is_contiguous(a) && + a->ne[2] > 1 && a->ne[3] > 1 && src0_type == GGML_TYPE_F16) { + return false; } + // TODO: This specific configuration can fail with oneDNN and needs more debugging if (!ggml_is_permuted(a) && ggml_is_permuted(b) && b->ne[2] > 1 && b->ne[3] > 1 && a->ne[0] > 128 && a->ne[2] == 1 && src0_type == GGML_TYPE_F16) { @@ -4553,9 +4564,9 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_VIEW: case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: - return true; case GGML_OP_ADD: case GGML_OP_ADD1: + case GGML_OP_ADD_ID: case GGML_OP_SUB: case GGML_OP_COUNT_EQUAL: case GGML_OP_MUL: diff --git a/ggml/src/ggml-sycl/mmvq.cpp b/ggml/src/ggml-sycl/mmvq.cpp index 5b7f064074..316aa0d0fb 100644 --- a/ggml/src/ggml-sycl/mmvq.cpp +++ b/ggml/src/ggml-sycl/mmvq.cpp @@ -595,6 +595,25 @@ static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy, } } +static void mul_mat_vec_mxfp4_q8_1_sycl(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_MXFP4 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + + { + stream->submit([&](sycl::handler & cgh) { + cgh.parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1); + }); + }); + } +} + + static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, @@ -1123,6 +1142,9 @@ void ggml_sycl_op_mul_mat_vec_q(ggml_backend_sycl_context & ctx, const ggml_tens case GGML_TYPE_IQ4_XS: mul_mat_vec_iq4_xs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; + case GGML_TYPE_MXFP4: + mul_mat_vec_mxfp4_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); + break; default: GGML_ABORT("fatal error"); } diff --git a/ggml/src/ggml-sycl/pad.cpp b/ggml/src/ggml-sycl/pad.cpp index 413712c584..f989c5e4b8 100644 --- a/ggml/src/ggml-sycl/pad.cpp +++ b/ggml/src/ggml-sycl/pad.cpp @@ -14,10 +14,10 @@ #include "pad.hpp" static void pad_f32(const float * src, float * dst, - const int lp0, const int rp0, const int lp1, const int rp1, - const int lp2, const int rp2, const int lp3, const int rp3, - const int ne0, const int ne1, const int ne2, const int ne3) { - auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const int lp0, const int rp0, const int lp1, const int rp1, + const int lp2, const int rp2, const int lp3, const int rp3, + const int ne0, const int ne1, const int ne2, const int ne3, + sycl::nd_item<3> item_ct1) { int i0 = item_ct1.get_local_id(2) + item_ct1.get_group(2) * item_ct1.get_local_range(2); int i1 = item_ct1.get_group(1); @@ -63,7 +63,7 @@ static void pad_f32_sycl(const float *src, float *dst, const int lp0, sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { pad_f32(src, dst, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, ne0, ne1, - ne2, ne3); + ne2, ne3, item_ct1); }); } diff --git a/ggml/src/ggml-sycl/softmax.cpp b/ggml/src/ggml-sycl/softmax.cpp index 83b7c71b66..b41124acc1 100644 --- a/ggml/src/ggml-sycl/softmax.cpp +++ b/ggml/src/ggml-sycl/softmax.cpp @@ -277,7 +277,7 @@ static void soft_max_f32_sycl(const float *x, const T *mask, const int id = get_current_device_id(); const size_t smpbo = ggml_sycl_info().devices[id].smpbo; - if (nbytes_shared <= smpbo) { + if (nbytes_shared <= smpbo && ncols_x <= max_block_size) { launch_soft_max_kernels<32, 64, 128, 256, 512, 1024, 2048, 4096>( x, mask, sinks, dst, params, stream, block_dims, block_nums, nbytes_shared); diff --git a/ggml/src/ggml-sycl/ssm_conv.cpp b/ggml/src/ggml-sycl/ssm_conv.cpp index 0dc0f71c9a..eea9a73d67 100644 --- a/ggml/src/ggml-sycl/ssm_conv.cpp +++ b/ggml/src/ggml-sycl/ssm_conv.cpp @@ -88,7 +88,7 @@ void ggml_sycl_ssm_conv(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { GGML_ASSERT(src0->nb[0] == sizeof(float)); GGML_ASSERT(src1->nb[0] == sizeof(float)); - GGML_ASSERT(src0->nb[1] == src0->ne[0] * static_cast(sizeof(float))); + GGML_ASSERT(src0->nb[1] == src0->ne[0] * sizeof(float)); const int src_stride_inner = ncs; const int src_stride_seq = ncs * d_inner; diff --git a/ggml/src/ggml-sycl/vecdotq.hpp b/ggml/src/ggml-sycl/vecdotq.hpp index 4088ddb54f..43482b3672 100644 --- a/ggml/src/ggml-sycl/vecdotq.hpp +++ b/ggml/src/ggml-sycl/vecdotq.hpp @@ -20,6 +20,18 @@ typedef float (*vec_dot_q_sycl_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs); +static __dpct_inline__ int get_int_b1(const void * x, const int & i32) { + const uint8_t * x8 = (const uint8_t *) x; + + int x32 = x8[4*i32 + 0] << 0; + x32 |= x8[4*i32 + 1] << 8; + x32 |= x8[4*i32 + 2] << 16; + x32 |= x8[4*i32 + 3] << 24; + + return x32; +} + + static __dpct_inline__ int get_int_from_int8(const int8_t* x8, const int& i32) { const uint16_t* x16 = (const uint16_t*)(x8 + sizeof(int) * i32); // assume at least 2 byte @@ -75,6 +87,28 @@ static __dpct_inline__ void get_int_from_table_16(const uint32_t &q4, val2 = v1 | (v2 << 16); } +static __dpct_inline__ sycl::int2 get_int_from_table_16( + const int& q4, const int8_t* table) { + const uint32_t* table32 = (const uint32_t*)table; + uint32_t tmp[2]; + const uint32_t low_high_selection_indices = + (0x32103210 | ((q4 & 0x88888888) >> 1)); +#pragma unroll + for (uint32_t i = 0; i < 2; ++i) { + const uint32_t shift = 16 * i; + + const uint32_t low = + dpct::byte_level_permute(table32[0], table32[1], q4 >> shift); + const uint32_t high = + dpct::byte_level_permute(table32[2], table32[3], q4 >> shift); + tmp[i] = dpct::byte_level_permute( + low, high, low_high_selection_indices >> shift); + } + return sycl::int2( + dpct::byte_level_permute(tmp[0], tmp[1], 0x6420), + dpct::byte_level_permute(tmp[0], tmp[1], 0x7531)); +} + #define VDR_Q2_K_Q8_1_MMVQ 1 // contiguous v/x values @@ -685,6 +719,30 @@ vec_dot_q4_1_q8_1(const void *__restrict__ vbq, return vec_dot_q4_1_q8_1_impl(v, u, bq4_1->dm, bq8_1->ds); } +#define VDR_MXFP4_Q8_1_MMVQ 2 +#define VDR_MXFP4_Q8_1_MMQ 4 + +static __dpct_inline__ float vec_dot_mxfp4_q8_1(const void * __restrict__ vbq, + const block_q8_1 * __restrict__ bq8_1, + const int & iqs) { + const block_mxfp4 * bq4 = (const block_mxfp4 *) vbq; + + const int * q8 = (const int *) bq8_1->qs + iqs; + + int sumi = 0; +#pragma unroll + for (int l = 0; l < VDR_MXFP4_Q8_1_MMVQ; ++l) { + const int aux_q4 = get_int_b1(bq4->qs, iqs + l); + const sycl::int2 v = get_int_from_table_16(aux_q4, kvalues_mxfp4); + sumi = ggml_sycl_dp4a(v.x(), q8[l + 0], sumi); + sumi = ggml_sycl_dp4a(v.y(), q8[l + 4], sumi); + } + + const float d = ggml_sycl_e8m0_to_fp32(bq4->e) * 0.5f * (bq8_1->ds)[0]; + return d * sumi; +} + + static __dpct_inline__ float vec_dot_q5_0_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs) { diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index c6f5809ccd..34ec09d403 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -659,6 +659,7 @@ struct vk_device_struct { vk_pipeline pipeline_cos_f32; vk_pipeline pipeline_log[2]; vk_pipeline pipeline_tri[2]; + vk_pipeline pipeline_diag[2]; vk_pipeline pipeline_clamp_f32; vk_pipeline pipeline_pad_f32; vk_pipeline pipeline_roll_f32; @@ -722,6 +723,11 @@ struct vk_device_struct { vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16; vk_pipeline pipeline_soft_max_f32_wg512, pipeline_soft_max_f32_f16_wg512; vk_pipeline pipeline_soft_max_back_f32; + + vk_pipeline pipeline_soft_max_large1_f32, pipeline_soft_max_large1_f32_f16; + vk_pipeline pipeline_soft_max_large2_f32, pipeline_soft_max_large2_f32_f16; + vk_pipeline pipeline_soft_max_large3_f32, pipeline_soft_max_large3_f32_f16; + vk_pipeline pipeline_rope_norm_f32, pipeline_rope_norm_f16, pipeline_rope_norm_f32_f16; vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16, pipeline_rope_neox_f32_f16; vk_pipeline pipeline_rope_multi_f32, pipeline_rope_multi_f16; @@ -757,7 +763,8 @@ struct vk_device_struct { vk_pipeline pipeline_flash_attn_split_k_reduce; - vk_pipeline pipeline_topk_moe[num_topk_moe_pipelines][TOPK_MOE_COUNT]; + // [2] is for whether to take n_experts from spec constant (0) or push constant (1) + vk_pipeline pipeline_topk_moe[num_topk_moe_pipelines][TOPK_MOE_COUNT][2]; std::vector all_pipelines; @@ -1149,6 +1156,7 @@ static_assert(sizeof(vk_op_multi_add_push_constants) <= 256); struct vk_op_topk_moe_push_constants { uint32_t n_rows; + uint32_t n_experts_push; uint32_t n_expert_used; float clamp_min; float clamp_max; @@ -3730,6 +3738,7 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_XS], "get_rows_iq4_xs", get_rows_iq4_xs_len, get_rows_iq4_xs_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_NL], "get_rows_iq4_nl", get_rows_iq4_nl_len, get_rows_iq4_nl_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_MXFP4], "get_rows_mxfp4", get_rows_mxfp4_len, get_rows_mxfp4_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_I32], "get_rows_i32", get_rows_i32_len, get_rows_i32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f32_f32", get_rows_f32_f32_len, get_rows_f32_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F16 ], "get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); @@ -3917,6 +3926,9 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_tri[0], "tri_f32", tri_f32_len, tri_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_tri[1], "tri_f16", tri_f16_len, tri_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_diag[0], "diag_f32", diag_f32_len, diag_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_diag[1], "diag_f16", diag_f16_len, diag_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_pad_f32, "pad_f32", pad_f32_len, pad_f32_data, "main", 2, sizeof(vk_op_pad_push_constants), {512, 1, 1}, {}, 1); @@ -3996,6 +4008,13 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16_wg512, "soft_max_f32_f16_wg512", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1); ggml_vk_create_pipeline(device, device->pipeline_soft_max_back_f32, "soft_max_back_f32", soft_max_back_f32_len, soft_max_back_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_large1_f32, "soft_max_large1_f32", soft_max_large1_f32_len, soft_max_large1_f32_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_large2_f32, "soft_max_large2_f32", soft_max_large2_f32_len, soft_max_large2_f32_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_large3_f32, "soft_max_large3_f32", soft_max_large3_f32_len, soft_max_large3_f32_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_large1_f32_f16, "soft_max_large1_f32_f16", soft_max_large1_f32_f16_len, soft_max_large1_f32_f16_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_large2_f32_f16, "soft_max_large2_f32_f16", soft_max_large2_f32_f16_len, soft_max_large2_f32_f16_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_large3_f32_f16, "soft_max_large3_f32_f16", soft_max_large3_f32_f16_len, soft_max_large3_f32_f16_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f32, "rope_norm_f32", rope_norm_f32_len, rope_norm_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_rope_multi_f32, "rope_multi_f32", rope_multi_f32_len, rope_multi_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); @@ -4204,10 +4223,12 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_whcn_f16_f32, "conv2d_dw_whcn_f16_f32", conv2d_dw_whcn_f16_f32_len, conv2d_dw_whcn_f16_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_cwhn_f16_f32, "conv2d_dw_cwhn_f16_f32", conv2d_dw_cwhn_f16_f32_len, conv2d_dw_cwhn_f16_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1); - for (uint32_t i = 0; i < num_topk_moe_pipelines; ++i) { - ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX], "topk_moe_f32_early_softmax_"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<subgroup_size); - ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX_NORM], "topk_moe_f32_early_softmax_norm"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<subgroup_size); - ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_LATE_SOFTMAX], "topk_moe_f32_late_softmax"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<subgroup_size); + for (uint32_t use_push = 0; use_push < 2; ++use_push) { + for (uint32_t i = 0; i < num_topk_moe_pipelines; ++i) { + ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX][use_push], "topk_moe_f32_early_softmax_"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<subgroup_size); + ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX_NORM][use_push], "topk_moe_f32_early_softmax_norm"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<subgroup_size); + ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_LATE_SOFTMAX][use_push], "topk_moe_f32_late_softmax"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<subgroup_size); + } } for (auto &c : compiles) { @@ -8274,6 +8295,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const switch (op) { case GGML_OP_GET_ROWS: GGML_ASSERT(src1->type == GGML_TYPE_I32); + if (src0->type == GGML_TYPE_I32) { + // i32 src only supports i32 result + GGML_ASSERT(dst->type == GGML_TYPE_I32); + return ctx->device->pipeline_get_rows[src0->type]; + } if (dst->type == GGML_TYPE_F16) { return ctx->device->pipeline_get_rows[src0->type]; } @@ -8400,6 +8426,12 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_tri[dst->type == GGML_TYPE_F16]; } return nullptr; + case GGML_OP_DIAG: + if (src0->type == dst->type && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) { + return ctx->device->pipeline_diag[dst->type == GGML_TYPE_F16]; + } + return nullptr; case GGML_OP_CLAMP: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_clamp_f32; @@ -8554,7 +8586,9 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const uint32_t idx = (uint32_t)ceilf(log2f(float(dst->ne[0]))); GGML_ASSERT(idx < num_topk_moe_pipelines); topk_moe_mode mode = ggml_vk_num_additional_ops_to_topk_moe_mode(ctx->num_additional_fused_ops); - return ctx->device->pipeline_topk_moe[idx][mode]; + // use n_experts from push constant if it's not equal to the power of two spec constant + bool use_push = dst->ne[0] != (1u << idx); + return ctx->device->pipeline_topk_moe[idx][mode][use_push]; } if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) { @@ -9091,6 +9125,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co case GGML_OP_COS: case GGML_OP_LOG: case GGML_OP_TRI: + case GGML_OP_DIAG: case GGML_OP_CLAMP: case GGML_OP_PAD: case GGML_OP_ROLL: @@ -9778,6 +9813,12 @@ static void ggml_vk_tri(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_TRI, std::move(p)); } +static void ggml_vk_diag(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst, ggml_nelements(dst)); + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_DIAG, std::move(p)); +} + static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst); p.param1 = ggml_get_op_params_f32(dst, 0); @@ -10111,7 +10152,7 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx, const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - ggml_vk_op_f32(ctx, subctx, src0, src1, src2, nullptr, dst, GGML_OP_SOFT_MAX, { + vk_op_soft_max_push_constants pc { ncols, src1 != nullptr ? nrows_y : (uint32_t)0, (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], @@ -10122,7 +10163,55 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx, n_head_log2, nrows_x, src2 != nullptr - }); + }; + + if (ncols <= 16384) { + ggml_vk_op_f32(ctx, subctx, src0, src1, src2, nullptr, dst, GGML_OP_SOFT_MAX, std::move(pc)); + } else { + + vk_subbuffer buf_a = ggml_vk_tensor_subbuffer(ctx, src0); + vk_subbuffer buf_b = src1 ? ggml_vk_tensor_subbuffer(ctx, src1) : buf_a; + vk_subbuffer buf_c = src2 ? ggml_vk_tensor_subbuffer(ctx, src2) : buf_a; + vk_subbuffer buf_d = ggml_vk_tensor_subbuffer(ctx, dst); + + uint32_t elems_per_wg = 128 * 4; + uint32_t num_wgs = CEIL_DIV(ncols, elems_per_wg); + size_t tmp_size = num_wgs * nrows_x * sizeof(float); + + if (ctx->prealloc_size_x < tmp_size) { + ctx->prealloc_size_x = tmp_size; + ggml_vk_preallocate_buffers(ctx, subctx); + } + if (ctx->prealloc_size_y < tmp_size) { + ctx->prealloc_size_y = tmp_size; + ggml_vk_preallocate_buffers(ctx, subctx); + } + if (ctx->prealloc_x_need_sync || ctx->prealloc_y_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + + vk_subbuffer buf_x = { ctx->prealloc_x, 0, tmp_size }; + vk_subbuffer buf_y = { ctx->prealloc_y, 0, tmp_size }; + + std::array elements = { num_wgs, nrows_x, 1 }; + + vk_pipeline pipeline1 = src1 && src1->type == GGML_TYPE_F16 ? ctx->device->pipeline_soft_max_large1_f32_f16 : ctx->device->pipeline_soft_max_large1_f32; + vk_pipeline pipeline2 = src1 && src1->type == GGML_TYPE_F16 ? ctx->device->pipeline_soft_max_large2_f32_f16 : ctx->device->pipeline_soft_max_large2_f32; + vk_pipeline pipeline3 = src1 && src1->type == GGML_TYPE_F16 ? ctx->device->pipeline_soft_max_large3_f32_f16 : ctx->device->pipeline_soft_max_large3_f32; + + ggml_pipeline_request_descriptor_sets(ctx, pipeline1, 1); + ggml_pipeline_request_descriptor_sets(ctx, pipeline2, 1); + ggml_pipeline_request_descriptor_sets(ctx, pipeline3, 1); + + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline1, { buf_a, buf_b, buf_c, buf_d, buf_x, buf_y }, pc, elements); + ggml_vk_sync_buffers(ctx, subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline2, { buf_a, buf_b, buf_c, buf_d, buf_x, buf_y }, pc, elements); + ggml_vk_sync_buffers(ctx, subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline3, { buf_a, buf_b, buf_c, buf_d, buf_x, buf_y }, pc, elements); + + ctx->prealloc_x_need_sync = true; + ctx->prealloc_y_need_sync = true; + } } static void ggml_vk_soft_max_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -10158,6 +10247,7 @@ static void ggml_vk_topk_moe(ggml_backend_vk_context * ctx, vk_context& subctx, vk_op_topk_moe_push_constants pc {}; pc.n_rows = n_rows; + pc.n_experts_push = n_experts; pc.n_expert_used = n_expert_used; if (mode == TOPK_MOE_EARLY_SOFTMAX_NORM) { ggml_tensor * clamp = cgraph->nodes[node_idx + 7]; @@ -11857,6 +11947,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr case GGML_OP_TRI: ggml_vk_tri(ctx, compute_ctx, src0, node); + break; + case GGML_OP_DIAG: + ggml_vk_diag(ctx, compute_ctx, src0, node); + break; case GGML_OP_CLAMP: ggml_vk_clamp(ctx, compute_ctx, src0, node); @@ -12832,8 +12926,7 @@ static bool ggml_vk_can_fuse_topk_moe(ggml_backend_vk_context * ctx, const struc } const int n_expert = softmax->ne[0]; - // n_expert must be a power of 2 - if (!is_pow2(n_expert) || n_expert > (1 << (num_topk_moe_pipelines-1))) { + if (n_expert > (1 << (num_topk_moe_pipelines-1))) { return false; } @@ -13877,6 +13970,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ4_NL: case GGML_TYPE_MXFP4: + case GGML_TYPE_I32: return true; default: return false; @@ -14001,6 +14095,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32; case GGML_OP_LOG: case GGML_OP_TRI: + case GGML_OP_DIAG: return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) && op->type == op->src[0]->type; case GGML_OP_ARGSORT: @@ -14591,6 +14686,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph * tensor_clone = ggml_log(ggml_ctx, src_clone[0]); } else if (tensor->op == GGML_OP_TRI) { tensor_clone = ggml_tri(ggml_ctx, src_clone[0], ggml_get_op_params_i32(tensor, 0)); + } else if (tensor->op == GGML_OP_DIAG) { + tensor_clone = ggml_diag(ggml_ctx, src_clone[0]); } else if (tensor->op == GGML_OP_CLAMP) { const float * params = (const float *)tensor->op_params; tensor_clone = ggml_clamp(ggml_ctx, src_clone[0], params[0], params[1]); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/diag.comp b/ggml/src/ggml-vulkan/vulkan-shaders/diag.comp new file mode 100644 index 0000000000..cd3f42f491 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/diag.comp @@ -0,0 +1,29 @@ +#version 450 + +#include "rte.glsl" +#include "types.glsl" +#include "generic_unary_head.glsl" + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +void main() { + const uint idx = get_idx(); + + if (idx >= p.ne) { + return; + } + + const uint i13 = fastdiv(idx, p.ne1_012mp, p.ne1_012L); + const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10; + const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, p.ne1_01L); + const uint i12_offset = i12*p.ne11*p.ne10; + const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, p.ne1_0L); + const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10; + + if (i10 == i11) { + const float val = float(data_a[get_aoffset() + i13*p.nb03 + i12*p.nb02 + 0*p.nb01 + i10*p.nb00]); + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(val); + } else { + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(0); + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp index 4bef48b006..0379e5d502 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp @@ -256,6 +256,9 @@ void main() { barrier(); } + // prevent race on tmpsh + barrier(); + // reduce across threads [[unroll]] for (uint32_t r = 0; r < Br; ++r) { diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp index cd82e4abfa..c995ab140e 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp @@ -302,6 +302,9 @@ void main() { barrier(); } + // prevent race on tmpsh + barrier(); + // reduce across threads float rowmaxf[rows_per_thread], eMf[rows_per_thread], Moldf[rows_per_thread]; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp index 76d83041ce..e88bdd057e 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp @@ -26,9 +26,9 @@ void main() { const uint d_offset = get_doffset() + i10*p.nb21 + i11*p.nb22 + i12*p.nb23; #if defined(DATA_A_BF16) - FLOAT_TYPE v = FLOAT_TYPE(bf16_to_fp32(data_a[a_offset + i00])); + TEMP_TYPE v = TEMP_TYPE(bf16_to_fp32(data_a[a_offset + i00])); #else - FLOAT_TYPE v = FLOAT_TYPE(data_a[a_offset + i00]); + TEMP_TYPE v = TEMP_TYPE(data_a[a_offset + i00]); #endif #ifndef OPTIMIZATION_ERROR_WORKAROUND data_d[d_offset + i00] = D_TYPE(v); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp index 0b74b33212..c5f5e9cbb2 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp @@ -7,34 +7,50 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; -void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows) { - const uint y_idx = i * QUANT_K + 32 * ib32; - - uint ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i; - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - const float d = float(data_a[ibi].d); - const uint qh = data_a[ibi].qh[ib32]; - const float dl = d * float(2 * bitfieldExtract(qh, 12, 3) + 1); - const float delta = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA; - +void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32, const uint i, + const uint num_blocks_per_row, const uint first_row, const uint num_rows) { + const uint y_idx_base = i * QUANT_K + 32 * ib32; + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + const uint base_b_idx = (j * p.batch_stride_b + b_offset + y_idx_base) / 4; [[unroll]] for (uint l = 0; l < 4; ++l) { - const uint qs = data_a[ibi].qs[4 * ib32 + l]; - const uint idxhi = bitfieldExtract(qh, 3 * int(l), 3); - const int16_t grid = int16_t(iq1s_grid[qs | (idxhi << 8)]); + const vec4 b_val_0 = vec4(data_b_v4[base_b_idx + 2 * l]); + const vec4 b_val_1 = vec4(data_b_v4[base_b_idx + 2 * l + 1]); - [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { - vec4 b0 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 0]); - vec4 b4 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 1]); + // index for data_a + uint ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i; + + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const float d = float(data_a[ibi].d); + const uint qh = data_a[ibi].qh[ib32]; + + const float dl = d * float(2 * bitfieldExtract(qh, 12, 3) + 1); + const uint qs = data_a[ibi].qs[4 * ib32 + l]; + const uint idxhi = bitfieldExtract(qh, 3 * int(l), 3); + const uint16_t grid = uint16_t(iq1s_grid[qs | (idxhi << 8)]); + + const float delta_val = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA; + const vec4 delta_v = vec4(delta_val); + const vec4 fbits0 = vec4( + float(bitfieldExtract(grid, 0, 2)), + float(bitfieldExtract(grid, 2, 2)), + float(bitfieldExtract(grid, 4, 2)), + float(bitfieldExtract(grid, 6, 2)) + ); + const vec4 fbits1 = vec4( + float(bitfieldExtract(grid, 8, 2)), + float(bitfieldExtract(grid, 10, 2)), + float(bitfieldExtract(grid, 12, 2)), + float(bitfieldExtract(grid, 14, 2)) + ); + + vec4 sum_v = fma(b_val_0, fbits0 + delta_v, vec4(0.0)); + sum_v = fma(b_val_1, fbits1 + delta_v, sum_v); + FLOAT_TYPE sum = dot(sum_v, vec4(1.0)); - FLOAT_TYPE sum = FLOAT_TYPE(0.0); - [[unroll]] for (int k = 0; k < 4; ++k) { - sum = fma(FLOAT_TYPE(b0[k]), bitfieldExtract(grid, 2 * k, 2) + delta, - fma(FLOAT_TYPE(b4[k]), bitfieldExtract(grid, 8 + 2 * k, 2) + delta, sum)); - } temp[j][n] = fma(dl, sum, temp[j][n]); + ibi += num_blocks_per_row; } } - ibi += num_blocks_per_row; } } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl index ee5ded2e8d..58ede04400 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl @@ -244,17 +244,20 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin const uint iqs = idx % 128; // 0..127 const uint n = iqs / 64; // 0,1 - const uint b = (iqs % 64) / 32; // 0,1 + const uint b = ((iqs % 64) / 32) * 4; // 0,4 const uint is_b = (iqs % 16) / 8; // 0,1 const uint qhshift = ((iqs % 64) / 16) * 2; // 0,2,4,6 const uint is = 8 * n + qhshift + is_b; // 0..15 - const uint qsi = n * 64 + (iqs % 32) * 2; // 0,2,4..126 - const uint qhi = n * 32 + (iqs % 16) * 2; // 0,2,4..62 + const uint qsi = n * 32 + (iqs % 32); // 0..63 + const uint qhi = n * 16 + (iqs % 16); // 0..31 const float dscale = float(data_a[ib].d) * float(data_a[ib].scales[is]); - buf_a[buf_idx] = FLOAT_TYPE_VEC2(dscale * float(int8_t(((data_a[ib].ql[qsi ] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi ] >> qhshift) & 3) << 4)) - 32), - dscale * float(int8_t(((data_a[ib].ql[qsi + 1] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi + 1] >> qhshift) & 3) << 4)) - 32)); + const uint ql = (uint(data_a_packed16[ib].ql[qsi]) >> b) & 0x0F0F; + const uint qh = (uint(data_a_packed16[ib].qh[qhi]) >> qhshift) & 0x0303; + const vec2 q = (vec2(unpack8(ql | (qh << 4)).xy) - 32) * dscale; + + buf_a[buf_idx] = FLOAT_TYPE_VEC2(q.x, q.y); #elif defined(DATA_A_IQ1_S) const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large1.comp new file mode 100644 index 0000000000..39c4663912 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large1.comp @@ -0,0 +1,62 @@ +#version 450 + +#include "soft_max_large_common.glsl" + +void main() { + const uint tid = gl_LocalInvocationID.x; + const uint rowx = gl_WorkGroupID.y; + const uint wg_start = gl_WorkGroupID.x * BLOCK_SIZE * num_iters; + + const uint32_t i03 = rowx / (p.ne01 * p.ne02); + const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01; + const uint32_t i01 = rowx % p.ne01; + + uint rowy_start = 0; + if (p.KY > 0) { + rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13; + } + + if (rowx >= p.nrows_x) { + return; + } + + float slope = get_slope(rowx); + + // Find max + FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02]; + + [[unroll]] for (uint col0 = wg_start, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) { + const uint col = col0 + tid; + + FLOAT_TYPE a = FLOAT_TYPE(0); + if (col < p.KX) { + a = data_a[rowx * p.KX + col]; + } + + FLOAT_TYPE b = FLOAT_TYPE(0); + if (p.KY > 0 && col < p.KX) { + b = data_b[rowy_start + col]; + } + + FLOAT_TYPE v = a * p.scale + slope * b; + + if (col < p.KX) { + max_val = max(max_val, v); + } + } + + // reduce across the workgroup + vals[tid] = max_val; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] = max(vals[tid], vals[tid + s]); + } + barrier(); + } + + if (tid == 0) { + max_val = vals[0]; + data_m[rowx * gl_NumWorkGroups.x + gl_WorkGroupID.x] = max_val; + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large2.comp new file mode 100644 index 0000000000..69524f5f75 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large2.comp @@ -0,0 +1,79 @@ +#version 450 + +#include "soft_max_large_common.glsl" + +void main() { + const uint tid = gl_LocalInvocationID.x; + const uint rowx = gl_WorkGroupID.y; + const uint wg_start = gl_WorkGroupID.x * BLOCK_SIZE * num_iters; + + const uint32_t i03 = rowx / (p.ne01 * p.ne02); + const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01; + const uint32_t i01 = rowx % p.ne01; + + uint rowy_start = 0; + if (p.KY > 0) { + rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13; + } + + if (rowx >= p.nrows_x) { + return; + } + + float slope = get_slope(rowx); + + // Find max + FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02]; + + [[unroll]] for (uint i = 0; i < gl_NumWorkGroups.x; i += BLOCK_SIZE) { + if (i + tid < gl_NumWorkGroups.x) { + max_val = max(max_val, data_m[rowx * gl_NumWorkGroups.x + i + tid]); + } + } + + // reduce across the workgroup + vals[tid] = max_val; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] = max(max_val, vals[tid + s]); + } + barrier(); + } + + max_val = vals[0]; + barrier(); + + FLOAT_TYPE sum = FLOAT_TYPE(0.0f); + + // Compute sum{exp(x - max)} + [[unroll]] for (uint col0 = wg_start, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) { + const uint col = col0 + tid; + + if (col >= p.KX) { + break; + } + + // compute exp(a*scale+b*slope), add it to sum + const uint i = rowx * p.KX + col; + FLOAT_TYPE val; + val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy_start + col]) : FLOAT_TYPE(0.0f)) - max_val); + sum += val; + data_d[i] = D_TYPE(val); + } + + // reduce across the workgroup + vals[tid] = sum; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] += vals[tid + s]; + } + barrier(); + } + + if (tid == 0) { + sum = vals[0]; + data_s[rowx * gl_NumWorkGroups.x + gl_WorkGroupID.x] = sum; + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large3.comp b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large3.comp new file mode 100644 index 0000000000..06efd7d9fb --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large3.comp @@ -0,0 +1,65 @@ +#version 450 + +#include "soft_max_large_common.glsl" + +shared FLOAT_TYPE sumsh[BLOCK_SIZE]; + +void main() { + const uint tid = gl_LocalInvocationID.x; + const uint rowx = gl_WorkGroupID.y; + const uint wg_start = gl_WorkGroupID.x * BLOCK_SIZE * num_iters; + + const uint32_t i03 = rowx / (p.ne01 * p.ne02); + const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01; + const uint32_t i01 = rowx % p.ne01; + + uint rowy_start = 0; + if (p.KY > 0) { + rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13; + } + + if (rowx >= p.nrows_x) { + return; + } + + FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02]; + FLOAT_TYPE sum = FLOAT_TYPE(0.0f); + + [[unroll]] for (uint i = 0; i < gl_NumWorkGroups.x; i += BLOCK_SIZE) { + if (i + tid < gl_NumWorkGroups.x) { + max_val = max(max_val, data_m[rowx * gl_NumWorkGroups.x + i + tid]); + sum += data_s[rowx * gl_NumWorkGroups.x + i + tid]; + } + } + + // reduce across the workgroup + vals[tid] = max_val; + sumsh[tid] = sum; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] = max(max_val, vals[tid + s]); + sumsh[tid] += sumsh[tid + s]; + } + barrier(); + } + + max_val = vals[0]; + sum = sumsh[0]; + + if (p.has_sinks != 0) { + sum += FLOAT_TYPE(exp(FLOAT_TYPE(data_c[i02]) - max_val)); + } + + FLOAT_TYPE rcpdivisor = 1.0/sum; + + [[unroll]] for (uint col0 = wg_start, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) { + const uint col = col0 + tid; + + if (col >= p.KX) { + continue; + } + + data_d[rowx*p.KX + col] *= D_TYPE(rcpdivisor); + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large_common.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large_common.glsl new file mode 100644 index 0000000000..6636d1f8de --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large_common.glsl @@ -0,0 +1,53 @@ +#extension GL_EXT_control_flow_attributes : enable + +layout (push_constant) uniform parameter +{ + uint KX; + uint KY; + uint ne00; + uint ne01; + uint ne02; + uint ne12; + uint ne13; + uint nb11; + uint nb12; + uint nb13; + float scale; + float max_bias; + float m0; + float m1; + uint n_head_log2; + uint nrows_x; + uint has_sinks; +} p; + +#include "types.glsl" + +layout(constant_id = 0) const uint BLOCK_SIZE = 128; +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; +layout(constant_id = 1) const uint num_iters = 4; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; +layout (binding = 2) readonly buffer Z {float data_c[];}; +layout (binding = 3) buffer D {D_TYPE data_d[];}; +layout (binding = 4) buffer M {float data_m[];}; +layout (binding = 5) buffer S {float data_s[];}; + +shared FLOAT_TYPE vals[BLOCK_SIZE]; + +float get_slope(uint rowx) { + float slope = 1.0f; + + // ALiBi + if (p.max_bias > 0.0f) { + const uint h = (rowx / p.ne01) % p.ne02; // head index + + const float base = h < p.n_head_log2 ? p.m0 : p.m1; + const uint exp = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1; + + slope = pow(base, exp); + } + + return slope; +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp b/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp index 5cd0785d20..b83a2b9d2d 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp @@ -10,6 +10,7 @@ layout (push_constant) uniform parameter { uint n_rows; + uint n_experts_push; uint n_expert_used; float clamp_min; float clamp_max; @@ -18,11 +19,16 @@ layout (push_constant) uniform parameter layout(local_size_x_id = 0, local_size_y = 4, local_size_z = 1) in; layout(constant_id = 0) const uint WARP_SIZE = 32; -layout(constant_id = 1) const uint n_experts = 512; +layout(constant_id = 1) const uint n_experts_spec = 512; layout(constant_id = 2) const bool with_norm = true; layout(constant_id = 3) const bool late_softmax = false; +layout(constant_id = 4) const bool nexperts_use_push = false; -const uint experts_per_thread = (n_experts > WARP_SIZE) ? n_experts / WARP_SIZE : 1; +uint n_experts = nexperts_use_push ? n_experts_push : n_experts_spec; + +#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b)) + +const uint experts_per_thread = CEIL_DIV(n_experts_spec, WARP_SIZE); layout (binding = 0, std430) readonly buffer Logits {float logits[];}; layout (binding = 1, std430) writeonly buffer Weights {float weights[];}; @@ -94,7 +100,7 @@ void main() { } if (!late_softmax) { - softmax_warp_inplace(wt, n_experts, lane, false); + softmax_warp_inplace(wt, n_experts, lane, nexperts_use_push); } // at this point, each thread holds a portion of softmax, diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index 92bae088b2..b0ade078c7 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -704,13 +704,15 @@ void process_shaders() { shader = (tname == "f32" || tname == "f16" || tname == "bf16") ? "get_rows.comp" : "get_rows_quant.comp"; if (tname == "f16") { - string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}})); + string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{"TEMP_TYPE", "FLOAT_TYPE"}, {data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}})); } else { - string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}})); + string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{"TEMP_TYPE", "FLOAT_TYPE"}, {data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}})); } - string_to_spv("get_rows_" + tname + "_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}})); + string_to_spv("get_rows_" + tname + "_f32", shader, merge_maps(base_dict, {{"TEMP_TYPE", "FLOAT_TYPE"}, {data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}})); } + string_to_spv("get_rows_i32", "get_rows.comp", {{"TEMP_TYPE", "uint"}, {"A_TYPE", "uint"}, {"B_TYPE", "int"}, {"D_TYPE", "uint"}}); + string_to_spv("mul_mat_vec_p021_f16_f32_subgroup_add", "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"A_TYPE_VEC4", "f16vec4"}, {"B_TYPE", "float"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}}); string_to_spv("mul_mat_vec_p021_f16_f32", "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"A_TYPE_VEC4", "f16vec4"}, {"B_TYPE", "float"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}}); string_to_spv("mul_mat_vec_nc_f16_f32", "mul_mat_vec_nc.comp", {{"A_TYPE", "float16_t"}, {"A_TYPE_VEC4", "f16vec4"}, {"B_TYPE", "float"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}}); @@ -854,6 +856,8 @@ void process_shaders() { string_to_spv("tri_f16", "tri.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); string_to_spv("tri_f32", "tri.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("diag_f16", "diag.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("diag_f32", "diag.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); string_to_spv("softplus_f16", "softplus.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); string_to_spv("softplus_f32", "softplus.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); @@ -899,6 +903,13 @@ void process_shaders() { string_to_spv("soft_max_f32_f16", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); string_to_spv("soft_max_back_f32", "soft_max_back.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_large1_f32", "soft_max_large1.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_large2_f32", "soft_max_large2.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_large3_f32", "soft_max_large3.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_large1_f32_f16", "soft_max_large1.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_large2_f32_f16", "soft_max_large2.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_large3_f32_f16", "soft_max_large3.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); + string_to_spv("rope_norm_f32", "rope_norm.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float"}}); string_to_spv("rope_norm_f16", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}}); string_to_spv("rope_norm_f16_rte", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}, {"RTE16", "1"}}); diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 9f5cdc1398..eb3ae72eaa 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -124,6 +124,13 @@ static void ggml_print_backtrace_symbols(void) { int nptrs = backtrace(trace, sizeof(trace)/sizeof(trace[0])); backtrace_symbols_fd(trace, nptrs, STDERR_FILENO); } +#elif defined(__APPLE__) +#include +static void ggml_print_backtrace_symbols(void) { + void * trace[100]; + int nptrs = backtrace(trace, sizeof(trace)/sizeof(trace[0])); + backtrace_symbols_fd(trace, nptrs, STDERR_FILENO); +} #else static void ggml_print_backtrace_symbols(void) { // platform not supported @@ -135,6 +142,20 @@ void ggml_print_backtrace(void) { if (GGML_NO_BACKTRACE) { return; } +#if defined(__APPLE__) + // On macOS, fork+debugger attachment is problematic due to: + // 1. libdispatch "poisons" forked child processes + // 2. lldb has issues attaching to parent from forked child + // Use simple backtrace() instead to avoid Terminal.app crashes + const char * GGML_BACKTRACE_LLDB = getenv("GGML_BACKTRACE_LLDB"); + if (!GGML_BACKTRACE_LLDB) { + fprintf(stderr, "WARNING: Using native backtrace. Set GGML_BACKTRACE_LLDB for more info.\n"); + fprintf(stderr, "WARNING: GGML_BACKTRACE_LLDB may cause native MacOS Terminal.app to crash.\n"); + fprintf(stderr, "See: https://github.com/ggml-org/llama.cpp/pull/17869\n"); + ggml_print_backtrace_symbols(); + return; + } +#endif #if defined(__linux__) FILE * f = fopen("/proc/self/status", "r"); size_t size = 0; @@ -5239,8 +5260,6 @@ struct ggml_tensor * ggml_flash_attn_ext( if (mask) { GGML_ASSERT(ggml_is_contiguous(mask)); - GGML_ASSERT(mask->ne[1] >= GGML_PAD(q->ne[1], GGML_KQ_MASK_PAD) && - "the Flash-Attention kernel requires the mask to be padded to GGML_KQ_MASK_PAD and at least n_queries big"); //GGML_ASSERT(ggml_can_repeat_rows(mask, qk)); GGML_ASSERT(q->ne[2] % mask->ne[2] == 0); @@ -7547,6 +7566,11 @@ size_t ggml_quantize_chunk( //////////////////////////////////////////////////////////////////////////////// +void ggml_log_get(ggml_log_callback * log_callback, void ** user_data) { + *log_callback = g_logger_state.log_callback; + *user_data = g_logger_state.log_callback_user_data; +} + void ggml_log_set(ggml_log_callback log_callback, void * user_data) { g_logger_state.log_callback = log_callback ? log_callback : ggml_log_callback_default; g_logger_state.log_callback_user_data = user_data; diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 2b8489c591..8ef4a23a10 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -3320,6 +3320,7 @@ class VisionProjectorType: ULTRAVOX = "ultravox" INTERNVL = "internvl" QWEN2A = "qwen2a" # audio + GLMA = "glma" # audio QWEN25O = "qwen2.5o" # omni VOXTRAL = "voxtral" LFM2 = "lfm2" diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index d9c87da194..b320e2b4b2 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -154,7 +154,8 @@ class TensorNameMap: "model.layers.{bid}.operator_norm", # lfm2 "model.transformer.blocks.{bid}.attn_norm", # llada "layers.{bid}.input_layernorm", # qwen3-embedding - "model.layers.{bid}.attention_layernorm" # apertus + "model.layers.{bid}.attention_layernorm", # apertus + "model.layers.{bid}.pre_attention_layernorm", # kormo ), # Attention norm 2 @@ -342,6 +343,7 @@ class TensorNameMap: "model.transformer.blocks.{bid}.ff_norm", # llada "layers.{bid}.post_attention_layernorm", # qwen3-embedding "model.layers.{bid}.feedforward_layernorm", # apertus + "model.layers.{bid}.pre_mlp_layernorm", # kormo ), # Pre feed-forward norm diff --git a/grammars/README.md b/grammars/README.md index a63198b5ae..daac7f4d8d 100644 --- a/grammars/README.md +++ b/grammars/README.md @@ -1,6 +1,6 @@ # GBNF Guide -GBNF (GGML BNF) is a format for defining [formal grammars](https://en.wikipedia.org/wiki/Formal_grammar) to constrain model outputs in `llama.cpp`. For example, you can use it to force the model to generate valid JSON, or speak only in emojis. GBNF grammars are supported in various ways in `tools/main` and `tools/server`. +GBNF (GGML BNF) is a format for defining [formal grammars](https://en.wikipedia.org/wiki/Formal_grammar) to constrain model outputs in `llama.cpp`. For example, you can use it to force the model to generate valid JSON, or speak only in emojis. GBNF grammars are supported in various ways in `tools/cli`, `tools/completion` and `tools/server`. ## Background @@ -67,6 +67,30 @@ Parentheses `()` can be used to group sequences, which allows for embedding alte - `{m,n}` repeats the precedent symbol or sequence at between `m` and `n` times (included) - `{0,n}` repeats the precedent symbol or sequence at most `n` times (included) +## Tokens + +Tokens allow grammars to match specific tokenizer tokens rather than character sequences. This is useful for constraining outputs based on special tokens (like `` or ``). + +Tokens can be specified in two ways: + +1. **Token ID**: Use angle brackets with the token ID in square brackets: `<[token-id]>`. For example, `<[1000]>` matches the token with ID 1000. + +2. **Token string**: Use angle brackets with the token text directly: ``. For example, `` will match the token whose text is exactly ``. This only works if the string tokenizes to exactly one token in the vocabulary, otherwise the grammar will fail to parse. + +You can negate token matches using the `!` prefix: `!<[1000]>` or `!` matches any token *except* the specified one. + +``` +# Match a thinking block: ... +# Using token strings (requires these to be single tokens in the vocab) +root ::= thinking .* +thinking ::= !* + +# Equivalent grammar using explicit token IDs +# Assumes token 1000 = , token 1001 = +root ::= <[1000]> thinking <[1001]> .* +thinking ::= !<[1001]>* +``` + ## Comments and newlines Comments can be specified with `#`: @@ -111,7 +135,7 @@ While semantically correct, the syntax `x? x? x?.... x?` (with N repetitions) ma You can use GBNF grammars: - In [llama-server](../tools/server)'s completion endpoints, passed as the `grammar` body field -- In [llama-cli](../tools/main), passed as the `--grammar` & `--grammar-file` flags +- In [llama-cli](../tools/cli) and [llama-completion](../tools/completion), passed as the `--grammar` & `--grammar-file` flags - With [test-gbnf-validator](../tests/test-gbnf-validator.cpp), to test them against strings. ## JSON Schemas → GBNF @@ -121,7 +145,7 @@ You can use GBNF grammars: - In [llama-server](../tools/server): - For any completion endpoints, passed as the `json_schema` body field - For the `/chat/completions` endpoint, passed inside the `response_format` body field (e.g. `{"type", "json_object", "schema": {"items": {}}}` or `{ type: "json_schema", json_schema: {"schema": ...} }`) -- In [llama-cli](../tools/main), passed as the `--json` / `-j` flag +- In [llama-cli](../tools/cli) and [llama-completion](../tools/completion), passed as the `--json` / `-j` flag - To convert to a grammar ahead of time: - in CLI, with [examples/json_schema_to_grammar.py](../examples/json_schema_to_grammar.py) - in JavaScript with [json-schema-to-grammar.mjs](../tools/server/public_legacy/json-schema-to-grammar.mjs) (this is used by the [server](../tools/server)'s Web UI) diff --git a/include/llama.h b/include/llama.h index b52eaacfa7..f862930099 100644 --- a/include/llama.h +++ b/include/llama.h @@ -313,6 +313,7 @@ extern "C" { bool check_tensors; // validate model tensor data bool use_extra_bufts; // use extra buffer types (used for weight repacking) bool no_host; // bypass host buffer allowing extra buffers to be used + bool no_alloc; // only load metadata and simulate memory allocations }; // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations @@ -466,10 +467,24 @@ extern "C" { // Frees all allocated memory LLAMA_API void llama_free(struct llama_context * ctx); + // fits mparams and cparams to free device memory (assumes system memory is unlimited) + // returns true if the parameters could be successfully modified to fit device memory + // this function is NOT thread safe because it modifies the global llama logger state + LLAMA_API bool llama_params_fit( + const char * path_model, + struct llama_model_params * mparams, + struct llama_context_params * cparams, + float * tensor_split, // writable buffer for tensor split, needs at least llama_max_devices elements + struct llama_model_tensor_buft_override * tensor_buft_overrides, // writable buffer for overrides, needs at least llama_max_tensor_buft_overrides elements + size_t margin, // margin of memory to leave per device in bytes + uint32_t n_ctx_min, // minimum context size to set when trying to reduce memory use + enum ggml_log_level log_level); // minimum log level to print during fitting, lower levels go to debug log + LLAMA_API int64_t llama_time_us(void); LLAMA_API size_t llama_max_devices(void); LLAMA_API size_t llama_max_parallel_sequences(void); + LLAMA_API size_t llama_max_tensor_buft_overrides(void); LLAMA_API bool llama_supports_mmap (void); LLAMA_API bool llama_supports_mlock (void); @@ -1354,7 +1369,9 @@ extern "C" { // Set callback for all future logging events. // If this is not called, or NULL is supplied, everything is output on stderr. - LLAMA_API void llama_log_set(ggml_log_callback log_callback, void * user_data); + // The logger state is global so these functions are NOT thread safe. + LLAMA_API void llama_log_get(ggml_log_callback * log_callback, void ** user_data); + LLAMA_API void llama_log_set(ggml_log_callback log_callback, void * user_data); // // Performance utils diff --git a/models/templates/mistralai-Ministral-3-14B-Reasoning-2512.jinja b/models/templates/mistralai-Ministral-3-14B-Reasoning-2512.jinja new file mode 100644 index 0000000000..beb4d612c7 --- /dev/null +++ b/models/templates/mistralai-Ministral-3-14B-Reasoning-2512.jinja @@ -0,0 +1,126 @@ +{#- Default system message if no system prompt is passed. #} +{%- set default_system_message = '# HOW YOU SHOULD THINK AND ANSWER\n\nFirst draft your thinking process (inner monologue) until you arrive at a response. Format your response using Markdown, and use LaTeX for any mathematical equations. Write both your thoughts and the response in the same language as the input.\n\nYour thinking process must follow the template below:[THINK]Your thoughts or/and draft, like working through an exercise on scratch paper. Be as casual and as long as you want until you are confident to generate the response to the user.[/THINK]Here, provide a self-contained response.' %} + +{#- Begin of sequence token. #} +{{- bos_token }} + +{#- Handle system prompt if it exists. #} +{#- System prompt supports text content or text and thinking chunks. #} +{%- if messages[0]['role'] == 'system' %} + {{- '[SYSTEM_PROMPT]' -}} + {%- if messages[0]['content'] is string %} + {{- messages[0]['content'] -}} + {%- else %} + {%- for block in messages[0]['content'] %} + {%- if block['type'] == 'text' %} + {{- block['text'] }} + {%- elif block['type'] == 'thinking' %} + {{- '[THINK]' + block['thinking'] + '[/THINK]' }} + {%- else %} + {{- raise_exception('Only text and thinking chunks are supported in system message contents.') }} + {%- endif %} + {%- endfor %} + {%- endif %} + {{- '[/SYSTEM_PROMPT]' -}} + {%- set loop_messages = messages[1:] %} +{%- else %} + {%- set loop_messages = messages %} + {%- if default_system_message != '' %} + {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} + {%- endif %} +{%- endif %} + + +{#- Tools definition #} +{%- set tools_definition = '' %} +{%- set has_tools = false %} +{%- if tools is defined and tools is not none and tools|length > 0 %} + {%- set has_tools = true %} + {%- set tools_definition = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} + {{- tools_definition }} +{%- endif %} + +{#- Checks for alternating user/assistant messages. #} +{%- set ns = namespace(index=0) %} +{%- for message in loop_messages %} + {%- if message.role == 'user' or (message.role == 'assistant' and (message.tool_calls is not defined or message.tool_calls is none or message.tool_calls | length == 0)) %} + {%- if (message['role'] == 'user') != (ns.index % 2 == 0) %} + {{- raise_exception('After the optional system message, conversation roles must alternate user and assistant roles except for tool calls and results.') }} + {%- endif %} + {%- set ns.index = ns.index + 1 %} + {%- endif %} +{%- endfor %} + +{#- Handle conversation messages. #} +{%- for message in loop_messages %} + + {#- User messages supports text content or text and image chunks. #} + {%- if message['role'] == 'user' %} + {%- if message['content'] is string %} + {{- '[INST]' + message['content'] + '[/INST]' }} + {%- elif message['content'] | length > 0 %} + {{- '[INST]' }} + {%- if message['content'] | length == 2 %} + {%- set blocks = message['content'] | sort(attribute='type') %} + {%- else %} + {%- set blocks = message['content'] %} + {%- endif %} + {%- for block in blocks %} + {%- if block['type'] == 'text' %} + {{- block['text'] }} + {%- elif block['type'] in ['image', 'image_url'] %} + {{- '[IMG]' }} + {%- else %} + {{- raise_exception('Only text, image and image_url chunks are supported in user message content.') }} + {%- endif %} + {%- endfor %} + {{- '[/INST]' }} + {%- else %} + {{- raise_exception('User message must have a string or a list of chunks in content') }} + {%- endif %} + + {#- Assistant messages supports text content or text, image and thinking chunks. #} + {%- elif message['role'] == 'assistant' %} + {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} + {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} + {%- endif %} + + {%- if message['content'] is string and message['content'] != '' %} + {{- message['content'] }} + {%- elif message['content'] | length > 0 %} + {%- for block in message['content'] %} + {%- if block['type'] == 'text' %} + {{- block['text'] }} + {%- elif block['type'] == 'thinking' %} + {{- '[THINK]' + block['thinking'] + '[/THINK]' }} + {%- else %} + {{- raise_exception('Only text and thinking chunks are supported in assistant message contents.') }} + {%- endif %} + {%- endfor %} + {%- endif %} + + {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} + {%- for tool in message['tool_calls'] %} + {{- '[TOOL_CALLS]' }} + {%- set name = tool['function']['name'] %} + {%- set arguments = tool['function']['arguments'] %} + {%- if arguments is not string %} + {%- set arguments = arguments|tojson|safe %} + {%- elif arguments == '' %} + {%- set arguments = '{}' %} + {%- endif %} + {{- name + '[ARGS]' + arguments }} + {%- endfor %} + {%- endif %} + + {{- eos_token }} + + {#- Tool messages only supports text content. #} + {%- elif message['role'] == 'tool' %} + {{- '[TOOL_RESULTS]' + message['content']|string + '[/TOOL_RESULTS]' }} + + {#- Raise exception for unsupported roles. #} + {%- else %} + {{- raise_exception('Only user, assistant and tool roles are supported, got ' + message['role'] + '.') }} + {%- endif %} +{%- endfor %} diff --git a/pyrightconfig.json b/pyrightconfig.json index 5320fe5864..a7bc007bdc 100644 --- a/pyrightconfig.json +++ b/pyrightconfig.json @@ -1,5 +1,5 @@ { - "extraPaths": ["gguf-py"], + "extraPaths": ["gguf-py", "examples/model-conversion/scripts"], "pythonVersion": "3.9", "pythonPlatform": "All", "reportUnusedImport": "warning", diff --git a/scripts/compare-logprobs.py b/scripts/compare-logprobs.py new file mode 100644 index 0000000000..63861dd9a4 --- /dev/null +++ b/scripts/compare-logprobs.py @@ -0,0 +1,281 @@ +import argparse +import requests +import json +from pathlib import Path +import logging + +logger = logging.getLogger("compare-logprobs") +logging.basicConfig(level=logging.INFO) + + +DESCRIPTION = """ +Compare logits between llama.cpp and another inference engine using OpenAI-compatible server endpoints. + +Unlike compare-logits.py, it allows dumping logits from a hosted API endpoint. Useful when it's not possible to run both models locally. + +Example usage: + Step 1: Dump logits from two different servers + python scripts/compare-logprobs.py dump logits_llama.log http://localhost:8080/v1/completions + python scripts/compare-logprobs.py dump logits_other.log http://other-engine:8000/v1/completions + + (optionally, you can add --api-key if the endpoint requires authentication) + + Step 2: Compare the dumped logits + python scripts/compare-logprobs.py compare logits_llama.log logits_other.log report.md +""" + + +def generate_input_prompt(length: int) -> list[str]: + CORPUS = """ + You are an advanced AI assistant capable of using tools to gather information, perform calculations, or execute tasks. Always think step by step before responding. If a user's query requires external data, computation, or actions beyond your internal knowledge, use the appropriate tools via function calls. + + ### Tool Call Format: + When you need to use a tool, output the call in this exact XML format. Include the opening and closing tags. Do not escape arguments; they will be parsed as plain text. + + You can make multiple calls in one go by placing them one after another. + """ + words = [w.strip() for w in CORPUS.strip().split(" ")] + words = [w for w in words if len(w) > 0] # filter out empty strings + while len(words) < length: + words += words + return words[:length] + + +def dump_logits( + endpoint: str, + output_path: Path, + input_words: list[str], + pattern: list[tuple[bool, int]], + api_key=None, +): + logger.info(f"Dumping logits to {output_path} from endpoint {endpoint}...") + words = input_words + curr_text = "" + n_total = sum(n for get, n in pattern if get) + n_done = 0 + i_cur = 0 + i_total = len(words) + with output_path.open("w") as f: + for get, n in pattern: + if not get: + # skip n words + for i in range(n): + curr_text += words.pop(0) + " " + i_cur += 1 + continue + # get n words + for i in range(n): + curr_text += words.pop(0) + " " + payload = { + "prompt": curr_text.strip(), + "temperature": 0.0, + "top_k": 1, + "max_tokens": 1, + "logprobs": 1, + "stream": False, + } + response = requests.post( + endpoint, + json=payload, + headers={"Authorization": f"Bearer {api_key}"} if api_key else {}, + ) + response.raise_for_status() + data = response.json() + data["__index"] = i_cur # add index for easier debugging later + data = json.dumps(data) + f.write(f"{data}\n") + n_done += 1 + i_cur += 1 + logger.info( + f"\n\n{data}\n\n[Step: {n_done}/{n_total} | Word: {i_cur}/{i_total}]" + ) + logger.info(f"Logits dumped to {output_path}") + + +def get_token_logprobs(data: dict): + logprobs = data["choices"][0]["logprobs"] + if "content" in logprobs: + # llama.cpp case + top = logprobs["content"][0]["top_logprobs"][0] + return top["token"], top["logprob"] + else: + # vllm case + tokens = logprobs["tokens"] + token_logprobs = logprobs["token_logprobs"] + return tokens[0], token_logprobs[0] + + +def clean_text(text: str) -> str: + return ( + "'" + + text.replace("\n", "\\n") + .replace("\t", "\\t") + .replace("\r", "\\r") + .replace("|", "\\|") + + "'" + ) + + +def compare_logits(input1: Path, input2: Path, output_path: Path): + with input1.open("r") as f1, input2.open("r") as f2, output_path.open("w") as fout: + lines1 = f1.readlines() + lines2 = f2.readlines() + + tab_header = [ + "idx", + input1.name, + "logprob_1", + input2.name, + "logprob_2", + "diff (abs)", + ] + tab_entries = [] + tab_max_widths = [len(h) for h in tab_header] + + assert len(lines1) == len( + lines2 + ), "Input files must have the same number of lines." + + fout.write("# Logits Comparison Report\n\n") + for i, (line1, line2) in enumerate(zip(lines1, lines2)): + if not line1.strip() or not line2.strip(): + continue # skip empty lines + + data1 = json.loads(line1) + data2 = json.loads(line2) + + idx1 = data1.get("__index", -1) + idx2 = data2.get("__index", -1) + if idx1 != idx2: + logger.warning( + f"Warning: Mismatched indices at line {i}: {idx1} vs {idx2}" + ) + + token1, logprob1 = get_token_logprobs(data1) + token2, logprob2 = get_token_logprobs(data2) + + token1 = clean_text(token1) + token2 = clean_text(token2) + abs_diff = abs(logprob1 - logprob2) + + tab_entries.append( + ( + str(idx1 + 1), + token1, + f"{logprob1:.4f}", + token2, + f"{logprob2:.4f}", + f"{(abs_diff):.4f}", + ) + ) + + for i in range(len(tab_entries)): + for j in range(len(tab_header)): + tab_max_widths[j] = max(tab_max_widths[j], len(tab_entries[i][j])) + + output = "" + for j in range(len(tab_header)): + output += f"| {tab_header[j]:<{tab_max_widths[j]}} " + output += "|\n" + for j in range(len(tab_header)): + output += f"|{'-' * (tab_max_widths[j] + 2)}" + output += "|\n" + for entry in tab_entries: + for j in range(len(tab_header)): + output += f"| {entry[j]:<{tab_max_widths[j]}} " + output += "|\n" + + logger.info("\n" + output) + fout.write(output) + logger.info(f"Report written to {output_path}") + + +def parse_pattern(pattern: str) -> list[tuple[bool, int]]: + parts = pattern.split(",") + result = [] + for i, part in enumerate(parts): + n = int(part) + if i % 2 == 0: + result.append((True, n)) # get n words + else: + result.append((False, n)) # skip n words + return result + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description=DESCRIPTION, formatter_class=argparse.RawTextHelpFormatter + ) + subparsers = parser.add_subparsers( + dest="verb", required=True, help="action to perform" + ) + + # dump subcommand + parser_dump = subparsers.add_parser("dump", help="dump logits from an endpoint") + parser_dump.add_argument( + "output", type=Path, help="output path for dumped logits (.log)" + ) + parser_dump.add_argument( + "endpoint", type=str, help="OAI-compat /completions endpoint" + ) + parser_dump.add_argument( + "--api-key", + type=str, + default=None, + help="API key for authentication (if required)", + ) + parser_dump.add_argument( + "--file", + type=Path, + default=None, + help="File containing prompt to use instead of the default", + ) + parser_dump.add_argument( + "--pattern", + type=str, + default="10,1000,10,4000,10", + help="Pattern n_get,n_skip,... where n_get is number of words to get and n_skip is number of words to skip (num of words, NOT num of tokens)", + ) + + # compare subcommand + parser_compare = subparsers.add_parser( + "compare", help="compare two dumped logits files" + ) + parser_compare.add_argument("input1", type=Path, help="first input file (.log)") + parser_compare.add_argument("input2", type=Path, help="second input file (.log)") + parser_compare.add_argument( + "output", type=Path, help="output path for comparison report (.md)" + ) + + try: + return parser.parse_args() + except Exception as e: + parser.print_help() + raise e + + +def main(): + args = parse_args() + + if args.verb == "dump": + pattern = parse_pattern(args.pattern) + input_length = sum(n for _, n in pattern) + input_words = generate_input_prompt(input_length) + if args.file is not None: + with args.file.open("r") as f: + input_words = f.read().strip().split(" ") + if input_length < sum(n for _, n in pattern): + raise ValueError( + f"Input file has only {input_length} words, but pattern requires at least {input_length} words." + ) + input_length = len(input_words) + logger.info(f"Using {input_length} words") + dump_logits(args.endpoint, args.output, input_words, pattern, args.api_key) + elif args.verb == "compare": + compare_logits(args.input1, args.input2, args.output) + else: + raise ValueError(f"Unknown verb: {args.verb}") + + +if __name__ == "__main__": + main() diff --git a/scripts/snapdragon/adb/run-cli.sh b/scripts/snapdragon/adb/run-cli.sh index ab8d6d49a2..cc5e47c2d6 100755 --- a/scripts/snapdragon/adb/run-cli.sh +++ b/scripts/snapdragon/adb/run-cli.sh @@ -46,7 +46,7 @@ adb $adbserial shell " \ LD_LIBRARY_PATH=$basedir/$branch/lib \ ADSP_LIBRARY_PATH=$basedir/$branch/lib \ $verbose $experimental $sched $opmask $profile $nhvx $ndev \ - ./$branch/bin/llama-cli --no-mmap -m $basedir/../gguf/$model \ + ./$branch/bin/llama-completion --no-mmap -m $basedir/../gguf/$model \ --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 \ --ctx-size 8192 --batch-size 128 -ctk q8_0 -ctv q8_0 -fa on \ -ngl 99 --device $device $cli_opts $@ \ diff --git a/scripts/snapdragon/adb/run-mtmd.sh b/scripts/snapdragon/adb/run-mtmd.sh new file mode 100755 index 0000000000..91d868278a --- /dev/null +++ b/scripts/snapdragon/adb/run-mtmd.sh @@ -0,0 +1,65 @@ +#!/bin/sh +# + +# Basedir on device +basedir=/data/local/tmp/llama.cpp + +cli_opts= + +branch=. +[ "$B" != "" ] && branch=$B + +adbserial= +[ "$S" != "" ] && adbserial="-s $S" + +model="gemma-3-4b-it-Q4_0.gguf" +[ "$M" != "" ] && model="$M" + +mmproj="mmproj-F16.gguf" +[ "$MMPROJ" != "" ] && mmproj="$MMPROJ" + +image= +[ "$IMG" != "" ] && image="$IMG" + +device="HTP0" +[ "$D" != "" ] && device="$D" + +verbose= +[ "$V" != "" ] && verbose="GGML_HEXAGON_VERBOSE=$V" + +experimental="GGML_HEXAGON_EXPERIMENTAL=1" +[ "$E" != "" ] && experimental="GGML_HEXAGON_EXPERIMENTAL=$E" + +sched= +[ "$SCHED" != "" ] && sched="GGML_SCHED_DEBUG=2" cli_opts="$cli_opts -v" + +profile= +[ "$PROF" != "" ] && profile="GGML_HEXAGON_PROFILE=$PROF GGML_HEXAGON_OPSYNC=1" + +opmask= +[ "$OPMASK" != "" ] && opmask="GGML_HEXAGON_OPMASK=$OPMASK" + +nhvx= +[ "$NHVX" != "" ] && nhvx="GGML_HEXAGON_NHVX=$NHVX" + +ndev= +[ "$NDEV" != "" ] && ndev="GGML_HEXAGON_NDEV=$NDEV" + +# MTMD backend device for vision model (defaults to CPU if not set) +mtmd_backend= +[ "$MTMD_DEVICE" != "" ] && mtmd_backend="MTMD_BACKEND_DEVICE=$MTMD_DEVICE" + +set -x + +adb $adbserial shell " \ + cd $basedir; ulimit -c unlimited; \ + LD_LIBRARY_PATH=$basedir/$branch/lib \ + ADSP_LIBRARY_PATH=$basedir/$branch/lib \ + $verbose $experimental $sched $opmask $profile $nhvx $ndev $mtmd_backend \ + ./$branch/bin/llama-mtmd-cli --no-mmap -m $basedir/../gguf/$model \ + --mmproj $basedir/../gguf/$mmproj \ + --image $basedir/../gguf/$image \ + --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 \ + --ctx-size 8192 --batch-size 128 -ctk q8_0 -ctv q8_0 -fa on \ + -ngl 99 --device $device -v $cli_opts $@ \ +" diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index a879940eae..5823efac2d 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -55bc9320a4aae82af18e23eefd5de319a755d7b9 +130bc125a88bb57664b88932c48c38a1cb316fac diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt index fbd538109b..4192af7c0c 100644 --- a/src/CMakeLists.txt +++ b/src/CMakeLists.txt @@ -67,7 +67,7 @@ add_library(llama models/gemma-embedding.cpp models/gemma.cpp models/gemma2-iswa.cpp - models/gemma3-iswa.cpp + models/gemma3.cpp models/gemma3n-iswa.cpp models/glm4-moe.cpp models/glm4.cpp @@ -139,6 +139,7 @@ add_library(llama set_target_properties(llama PROPERTIES VERSION ${LLAMA_INSTALL_VERSION} SOVERSION 0 + MACHO_CURRENT_VERSION 0 # keep macOS linker from seeing oversized version number ) target_include_directories(llama PRIVATE .) diff --git a/src/llama-batch.cpp b/src/llama-batch.cpp index 86a1a4ba18..386fab04ac 100644 --- a/src/llama-batch.cpp +++ b/src/llama-batch.cpp @@ -695,6 +695,8 @@ llama_ubatch llama_batch_allocr::ubatch_add(const std::vector & idxs, u udata->seq_idx .resize(LLAMA_MAX_SEQ, -1); udata->output .resize(n_tokens); + udata->seq_id_data.reserve(n_tokens); + seq_set_t seq_set_unq; for (size_t i = 0; i < idxs.size(); ++i) { @@ -716,11 +718,13 @@ llama_ubatch llama_batch_allocr::ubatch_add(const std::vector & idxs, u } udata->n_seq_id[i] = batch.n_seq_id[idxs[i]]; - udata->seq_id[i] = batch.seq_id[idxs[i]]; udata->output[i] = batch.logits[idxs[i]]; for (int s = 0; s < udata->n_seq_id[i]; ++s) { - seq_set_unq.set(udata->seq_id[i][s]); + const llama_seq_id seq_id = batch.seq_id[idxs[i]][s]; + + udata->seq_id_data.push_back(seq_id); + seq_set_unq.set(seq_id); } if (udata->output[i]) { @@ -728,6 +732,12 @@ llama_ubatch llama_batch_allocr::ubatch_add(const std::vector & idxs, u } } + llama_seq_id * seq_id_ptr = udata->seq_id_data.data(); + for (size_t i = 0; i < idxs.size(); ++i) { + udata->seq_id[i] = seq_id_ptr; + seq_id_ptr += udata->n_seq_id[i]; + } + for (uint32_t s = 0; s < n_seq_max; ++s) { if (seq_set_unq.test(s)) { udata->seq_idx[s] = udata->seq_id_unq.size(); diff --git a/src/llama-batch.h b/src/llama-batch.h index 209cf3699d..8e6fac0efa 100644 --- a/src/llama-batch.h +++ b/src/llama-batch.h @@ -56,13 +56,15 @@ struct llama_ubatch { std::vector embd; std::vector pos; std::vector n_seq_id; - std::vector seq_id; + std::vector seq_id; // these point into the seq_id_data below std::vector seq_id_unq; std::vector seq_idx; std::vector output; + + std::vector seq_id_data; }; - // the llama_ubatch pointers above point to this data if set. otherwise - points to non-owning data + // the llama_ubatch pointers above point to this data if set. otherwise - point to external non-owning data std::shared_ptr data; }; diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 4171400713..8786d4ee3e 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -9,6 +9,7 @@ #include "llama-model.h" #include +#include #include #include #include @@ -72,6 +73,43 @@ llama_context::llama_context( cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f; } + if (cparams.yarn_ext_factor != 0) { + static auto get_mscale = [](float scale, float mscale) { + return scale <= 1.0f ? 1.0f : (0.1f * mscale * logf(scale) + 1.0f); + }; + + const float factor = 1.0f / cparams.rope_freq_scale; + + // ref: https://github.com/huggingface/transformers/blob/6d00f6b0a5679c36510f203e4226e36f517c3032/src/transformers/modeling_rope_utils.py#L336-L348 + if (hparams.rope_yarn_log_mul != 0.0f) { + // note: here we assume `mscale == 1.0f` + // TODO: start reading the actual value of mscale and handle the case where it is not 1.0f + float mscale = 1.0f; + const float mscale_all_dims = hparams.rope_yarn_log_mul; + + // [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX] + // special-case DEEPSEEK v2: + // https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat/blob/main/config.json#L42-L43 + if (model.arch == LLM_ARCH_DEEPSEEK2 && mscale_all_dims != 1.0f) { + mscale = mscale_all_dims; + } + + cparams.yarn_attn_factor = get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dims); + + LLAMA_LOG_WARN("%s: setting new yarn_attn_factor = %.4f (mscale == %.1f, mscale_all_dim = %.1f)\n", + __func__, cparams.yarn_attn_factor, mscale, mscale_all_dims); + } else { + cparams.yarn_attn_factor = get_mscale(factor, 1.0f); + } + + // when YARN is applied with yarn_ext_factor != 0.0f, we need to cancel this factor: + // https://github.com/ggml-org/llama.cpp/blob/a81a569577cc38b32558958b048228150be63eae/ggml/src/ggml-cpu/ops.cpp#L5541-L5544 + // + // ref: https://github.com/ggml-org/llama.cpp/discussions/7416 + // https://github.com/ggml-org/llama.cpp/pull/17945 + cparams.yarn_attn_factor *= 1.0f / (1.0f + 0.1f * logf(factor)); + } + cparams.yarn_attn_factor *= hparams.rope_attn_factor; if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) { @@ -93,14 +131,6 @@ llama_context::llama_context( // with causal attention, the batch size is limited by the context size cparams.n_batch = cparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch; - // the batch has to be at least GGML_KQ_MASK_PAD because we will be padding the KQ_mask - // this is required by GPU kernels in order to avoid out-of-bounds accesses (e.g. ggml_flash_attn_ext) - // ref: https://github.com/ggerganov/llama.cpp/pull/5021 - // TODO: this padding is not needed for the cache-less context so we should probably move it to llama_memory - if (cparams.n_batch < GGML_KQ_MASK_PAD) { - LLAMA_LOG_WARN("%s: n_batch is less than GGML_KQ_MASK_PAD - increasing to %d\n", __func__, GGML_KQ_MASK_PAD); - cparams.n_batch = GGML_KQ_MASK_PAD; - } cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch); cparams.op_offload = params.op_offload; @@ -228,6 +258,7 @@ llama_context::llama_context( backend_buft.clear(); backend_ptrs.clear(); + backend_buf_exp_size.clear(); for (auto & backend : backends) { auto * buft = ggml_backend_get_default_buffer_type(backend.get()); @@ -244,6 +275,7 @@ llama_context::llama_context( backend_buft.push_back(buft); backend_ptrs.push_back(backend.get()); + backend_buf_exp_size.push_back(0); } LLAMA_LOG_DEBUG("%s: backend_ptrs.size() = %zu\n", __func__, backend_ptrs.size()); @@ -359,7 +391,8 @@ llama_context::llama_context( // reserve pp (prompt processing) graph first so that buffers are only allocated once { - auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get()); + auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get(), + model.hparams.no_alloc, model.hparams.no_alloc ? backend_buf_exp_size.data() : nullptr); if (!gf) { if (pipeline_parallel) { LLAMA_LOG_WARN("%s: compute buffer allocation failed, retrying without pipeline parallelism\n", __func__); @@ -377,7 +410,7 @@ llama_context::llama_context( // reserve with tg (token generation) graph to get the number of splits and nodes { - auto * gf = graph_reserve(n_seqs, n_seqs, n_seqs, mctx.get()); + auto * gf = graph_reserve(n_seqs, n_seqs, n_seqs, mctx.get(), model.hparams.no_alloc); if (!gf) { throw std::runtime_error("failed to allocate compute tg buffers"); } @@ -392,7 +425,7 @@ llama_context::llama_context( // // auto * gf = graph_reserve(n_tokens, 1, n_tokens, mctx.get()); // - auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get()); + auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get(), model.hparams.no_alloc); if (!gf) { throw std::runtime_error("failed to allocate compute pp buffers"); } @@ -401,11 +434,13 @@ llama_context::llama_context( for (size_t i = 0; i < backend_ptrs.size(); ++i) { ggml_backend_t backend = backend_ptrs[i]; ggml_backend_buffer_type_t buft = backend_buft[i]; - size_t size = ggml_backend_sched_get_buffer_size(sched.get(), backend); - if (size > 1) { + if (!model.hparams.no_alloc) { + backend_buf_exp_size[i] = ggml_backend_sched_get_buffer_size(sched.get(), backend); + } + if (backend_buf_exp_size[i] > 1) { LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__, ggml_backend_buft_name(buft), - size / 1024.0 / 1024.0); + backend_buf_exp_size[i] / 1024.0 / 1024.0); } } @@ -424,6 +459,23 @@ llama_context::llama_context( } llama_context::~llama_context() { + // FIXME this currently results in a use-after-free bug if the model is freed before the context + // if (!model.hparams.no_alloc) { + // for (size_t i = 0; i < backend_ptrs.size(); ++i) { + // ggml_backend_t backend = backend_ptrs[i]; + // ggml_backend_buffer_type_t buft = backend_buft[i]; + + // const size_t size_exp = backend_buf_exp_size[i]; + // const size_t size_act = ggml_backend_sched_get_buffer_size(sched.get(), backend); + // if (size_exp == size_act) { + // LLAMA_LOG_DEBUG("%s: %10s compute buffer size is %8.4f MiB, matches expectation of %8.4f MiB\n", + // __func__, ggml_backend_buft_name(buft), size_act / (1024.0*1024.0), size_exp / (1024.0*1024.0)); + // } else { + // LLAMA_LOG_WARN("%s: %10s compute buffer size of %8.4f MiB, does not match expectation of %8.4f MiB\n", + // __func__, ggml_backend_buft_name(buft), size_act / (1024.0*1024.0), size_exp / (1024.0*1024.0)); + // } + // } + // } ggml_opt_free(opt_ctx); } @@ -1326,6 +1378,7 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) { // This doesn't happen often, but may be annoying in some cases (like the HellaSwag benchmark) LLAMA_LOG_INFO("%s: reallocating output buffer from size %.02f MiB to %.02f MiB\n", __func__, prev_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0); #endif + synchronize(); buf_output = nullptr; logits = nullptr; embd = nullptr; @@ -1397,7 +1450,8 @@ llm_graph_result * llama_context::get_gf_res_reserve() const { return static_cast(gf_res_reserve.get()); } -ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only) { +ggml_cgraph * llama_context::graph_reserve( + uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only, size_t * sizes) { LLAMA_LOG_DEBUG("%s: reserving a graph for ubatch with n_tokens = %4u, n_seqs = %2u, n_outputs = %4u\n", __func__, n_tokens, n_seqs, n_outputs); GGML_ASSERT(n_outputs >= 1); @@ -1434,8 +1488,13 @@ ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, u // initialize scheduler with the specified graph if (split_only) { - ggml_backend_sched_split_graph(sched.get(), gf); + if (sizes) { + ggml_backend_sched_reserve_size(sched.get(), gf, sizes); + } else { + ggml_backend_sched_split_graph(sched.get(), gf); + } } else if (!ggml_backend_sched_reserve(sched.get(), gf)) { + GGML_ASSERT(!sizes); LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__); return nullptr; } @@ -2057,15 +2116,26 @@ void llama_context::perf_reset() { std::map llama_context::memory_breakdown() const { std::map ret; - for (const auto & buft_size : model.memory_breakdown()) { - ret[buft_size.first].model += buft_size.second; + for (const auto & [buft, size] : model.memory_breakdown()) { + ret[buft].model += size; } - for (const auto & buft_size : memory->memory_breakdown()) { - ret[buft_size.first].context += buft_size.second; + if (memory) { + for (const auto & [buft, size] : memory->memory_breakdown()) { + ret[buft].context += size; + } } - for (const auto & backend_ptr : backends) { - ggml_backend_t backend = backend_ptr.get(); - ret[ggml_backend_sched_get_buffer_type(sched.get(), backend)].compute += ggml_backend_sched_get_buffer_size(sched.get(), backend); + if (model.hparams.no_alloc) { + for (size_t i = 0; i < backends.size(); ++i) { + ggml_backend_t backend = backends[i].get(); + ggml_backend_buffer_type_t buft = ggml_backend_sched_get_buffer_type(sched.get(), backend); + ret[buft].compute += backend_buf_exp_size[i]; + } + } else { + for (const auto & backend_ptr : backends) { + ggml_backend_t backend = backend_ptr.get(); + ggml_backend_buffer_type_t buft = ggml_backend_sched_get_buffer_type(sched.get(), backend); + ret[buft].compute += ggml_backend_sched_get_buffer_size(sched.get(), backend); + } } return ret; } diff --git a/src/llama-context.h b/src/llama-context.h index cd26eafe18..c31101330e 100644 --- a/src/llama-context.h +++ b/src/llama-context.h @@ -26,6 +26,10 @@ struct llama_memory_breakdown_data { size_t model = 0; // memory allocated for the model size_t context = 0; // memory allocated for the context size_t compute = 0; // memory allocated for temporary compute buffers + + size_t total() const { + return model + context + compute; + } }; struct llama_context { @@ -206,7 +210,8 @@ public: ggml_status graph_compute(ggml_cgraph * gf, bool batched); // reserve a graph with a dummy ubatch of the specified size - ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only = false); + ggml_cgraph * graph_reserve( + uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only = false, size_t * sizes = nullptr); private: llm_graph_params graph_params( @@ -281,9 +286,10 @@ private: std::vector> set_n_threads_fns; - // buffer types used for the compute buffer of each backend + // pointers and buffer types used for the compute buffer of each backend std::vector backend_ptrs; std::vector backend_buft; + std::vector backend_buf_exp_size; // expected buffer sizes llm_graph_result_ptr gf_res_prev; llm_graph_result_ptr gf_res_reserve; diff --git a/src/llama-grammar.cpp b/src/llama-grammar.cpp index b3c5eb5717..75d5d750c3 100644 --- a/src/llama-grammar.cpp +++ b/src/llama-grammar.cpp @@ -181,6 +181,52 @@ static std::pair parse_char(const char * src) { throw std::runtime_error("unexpected end of input"); } +static std::pair parse_token(const llama_vocab * vocab, const char * src) { + const char * pos = src; + if (*pos != '<') { + throw std::runtime_error(std::string("expecting '<' at ") + pos); + } + pos++; + + // Parse <[id]> + if (*pos == '[') { + pos++; + const char * int_end = parse_int(pos); + uint32_t token_id = std::stoul(std::string(pos, int_end - pos)); + pos = int_end; + if (*pos != ']') { + throw std::runtime_error(std::string("expecting ']' at ") + pos); + } + pos++; + if (*pos != '>') { + throw std::runtime_error(std::string("expecting '>' at ") + pos); + } + pos++; + return std::make_pair(token_id, pos); + } + + if (vocab == nullptr) { + throw std::runtime_error(std::string("no vocab to parse token at ") + src); + } + + // Parse and tokenize to obtain the token id + while (*pos != 0 && *pos != '>') { + pos++; + } + if (*pos != '>') { + throw std::runtime_error(std::string("expecting '>' at ") + pos); + } + pos++; + + llama_token tokens[2]; + int32_t n_tokens = vocab->tokenize(src, static_cast(pos - src), tokens, 2, false, true); + if (n_tokens != 1) { + // must tokenize to exactly 1 token + throw std::runtime_error("invalid token '" + std::string(src, pos - src) + "'"); + } + return std::make_pair(tokens[0], pos); +} + static void print_grammar_char(FILE * file, uint32_t c) { if (0x20 <= c && c <= 0x7f) { fprintf(file, "%c", static_cast(c)); @@ -212,6 +258,8 @@ static void print_rule_binary(FILE * file, const llama_grammar_rule & rule) { case LLAMA_GRETYPE_CHAR_RNG_UPPER: fprintf(file, "CHAR_RNG_UPPER"); break; case LLAMA_GRETYPE_CHAR_ALT: fprintf(file, "CHAR_ALT"); break; case LLAMA_GRETYPE_CHAR_ANY: fprintf(file, "CHAR_ANY"); break; + case LLAMA_GRETYPE_TOKEN: fprintf(file, "TOKEN"); break; + case LLAMA_GRETYPE_TOKEN_NOT: fprintf(file, "TOKEN_NOT"); break; } switch (elem.type) { case LLAMA_GRETYPE_END: @@ -228,6 +276,17 @@ static void print_rule_binary(FILE * file, const llama_grammar_rule & rule) { print_grammar_char(file, elem.value); fprintf(file, "\") "); break; + case LLAMA_GRETYPE_TOKEN: + fprintf(file, "<["); + fprintf(file, "%u", elem.value); + fprintf(file, "]> "); + break; + case LLAMA_GRETYPE_TOKEN_NOT: + fprintf(file, "!"); + fprintf(file, "<["); + fprintf(file, "%u", elem.value); + fprintf(file, "]> "); + break; } } fprintf(file, "\n"); @@ -284,6 +343,17 @@ static void print_rule( case LLAMA_GRETYPE_CHAR_ANY: fprintf(file, "."); break; + case LLAMA_GRETYPE_TOKEN: + fprintf(file, "<["); + fprintf(file, "%u", elem.value); + fprintf(file, "]> "); + break; + case LLAMA_GRETYPE_TOKEN_NOT: + fprintf(file, "!"); + fprintf(file, "<["); + fprintf(file, "%u", elem.value); + fprintf(file, "]> "); + break; } if (is_char_element(elem)) { switch (rule[i + 1].type) { @@ -444,6 +514,17 @@ const char * llama_grammar_parser::parse_sequence( } } pos = parse_space(pos + 1, is_nested); + } else if (*pos == '<' || *pos == '!') { // token + auto type = LLAMA_GRETYPE_TOKEN; + if (*pos == '!') { // token inverse + type = LLAMA_GRETYPE_TOKEN_NOT; + pos++; + } + auto token_pair = parse_token(vocab, pos); + const char * token_end = token_pair.second; + last_sym_start = rule.size(); + rule.push_back({type, token_pair.first}); + pos = parse_space(token_end, is_nested); } else if (is_word_char(*pos)) { // rule reference const char * name_end = parse_name(pos); uint32_t ref_rule_id = get_symbol_id(pos, name_end - pos); @@ -691,6 +772,21 @@ static bool llama_grammar_match_partial_char( return !is_positive_char; } +// returns true iff token matches the rule at pos (regular or inverse) +// asserts that pos is pointing to a token element +static bool llama_grammar_match_token( + const llama_grammar_element * pos, + const llama_token token) { + GGML_ASSERT(pos->type == LLAMA_GRETYPE_TOKEN || pos->type == LLAMA_GRETYPE_TOKEN_NOT); + if (pos->type == LLAMA_GRETYPE_TOKEN) { + return pos->value == static_cast(token); + } + if (pos->type == LLAMA_GRETYPE_TOKEN_NOT) { + return pos->value != static_cast(token); + } + return false; +} + // transforms a grammar pushdown stack into N possible stacks, all ending // at a character range (terminal element) static void llama_grammar_advance_stack( @@ -738,6 +834,8 @@ static void llama_grammar_advance_stack( case LLAMA_GRETYPE_CHAR: case LLAMA_GRETYPE_CHAR_NOT: case LLAMA_GRETYPE_CHAR_ANY: + case LLAMA_GRETYPE_TOKEN: + case LLAMA_GRETYPE_TOKEN_NOT: if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) { // only add the stack if it's not a duplicate of one we already have new_stacks.emplace_back(stack); @@ -831,26 +929,38 @@ llama_grammar_stacks & llama_grammar_get_stacks(struct llama_grammar * grammar) return grammar->stacks; } +static void llama_grammar_accept_chr( + struct llama_grammar & grammar, + const llama_grammar_stack & stack, + uint32_t chr, + llama_grammar_stacks & new_stacks) { + if (stack.empty()) { + return; + } + + const llama_grammar_element * pos = stack.back(); + + // ignore if this turns into a token + if (pos->type == LLAMA_GRETYPE_TOKEN || pos->type == LLAMA_GRETYPE_TOKEN_NOT) { + return; + } + + auto match = llama_grammar_match_char(pos, chr); + if (match.first) { + llama_grammar_stack new_stack(stack.begin(), stack.end() - 1); + if (!llama_grammar_is_end_of_sequence(match.second)) { + new_stack.push_back(match.second); + } + llama_grammar_advance_stack(grammar.rules, new_stack, new_stacks); + } +} + void llama_grammar_accept(struct llama_grammar * grammar, uint32_t chr) { llama_grammar_stacks stacks_new; stacks_new.reserve(grammar->stacks.size()); for (const auto & stack : grammar->stacks) { - if (stack.empty()) { - continue; - } - - auto match = llama_grammar_match_char(stack.back(), chr); - if (match.first) { - const llama_grammar_element * pos = match.second; - - // update top of stack to next element, if any - llama_grammar_stack new_stack(stack.begin(), stack.end() - 1); - if (!llama_grammar_is_end_of_sequence(pos)) { - new_stack.push_back(pos); - } - llama_grammar_advance_stack(grammar->rules, new_stack, stacks_new); - } + llama_grammar_accept_chr(*grammar, stack, chr, stacks_new); } grammar->stacks = std::move(stacks_new); @@ -875,6 +985,22 @@ llama_grammar_candidates llama_grammar_reject_candidates_for_stack( const llama_grammar_element * stack_pos = stack.back(); + // if the top of the stack is a token rule, then we only need to check the token id + if (stack_pos->type == LLAMA_GRETYPE_TOKEN || stack_pos->type == LLAMA_GRETYPE_TOKEN_NOT) { + for (const auto & tok : candidates) { + if (*tok.code_points == 0) { + // reached the end of a token consumed by char rules, reject iff it ended + // in a partial response + if (tok.partial_utf8.n_remain != 0) { + rejects.push_back(tok); + } + } else if (!llama_grammar_match_token(stack_pos, tok.id)) { + rejects.push_back(tok); + } + } + return rejects; + } + llama_grammar_candidates next_candidates; next_candidates.reserve(candidates.size()); @@ -887,7 +1013,7 @@ llama_grammar_candidates llama_grammar_reject_candidates_for_stack( rejects.push_back(tok); } } else if (llama_grammar_match_char(stack_pos, *tok.code_points).first) { - next_candidates.push_back({ tok.index, tok.code_points + 1, tok.partial_utf8 }); + next_candidates.push_back({ tok.index, tok.code_points + 1, tok.partial_utf8, tok.id }); } else { rejects.push_back(tok); } @@ -905,7 +1031,7 @@ llama_grammar_candidates llama_grammar_reject_candidates_for_stack( auto next_rejects = llama_grammar_reject_candidates(rules, next_stacks, next_candidates); for (const auto & tok : next_rejects) { - rejects.push_back({ tok.index, tok.code_points - 1, tok.partial_utf8 }); + rejects.push_back({ tok.index, tok.code_points - 1, tok.partial_utf8, tok.id }); } return rejects; @@ -972,12 +1098,13 @@ struct llama_grammar * llama_grammar_init_impl( vocab, std::move(vec_rules), std::move(stacks), - /* .partial_utf8 = */ {}, - /* .lazy =*/ false, - /* .awaiting_trigger = */ false, - /* .trigger_buffer = */ "", - /* .trigger_tokens = */ {}, - /* .trigger_patterns = */ {}, + /* .partial_utf8 = */ {}, + /* .lazy = */ false, + /* .awaiting_trigger = */ false, + /* .trigger_buffer = */ "", + /* .trigger_buffer_positions = */ {}, + /* .trigger_tokens = */ {}, + /* .trigger_patterns = */ {}, }; } @@ -990,7 +1117,7 @@ struct llama_grammar * llama_grammar_init_impl( size_t num_trigger_patterns, const llama_token * trigger_tokens, size_t num_trigger_tokens) { - llama_grammar_parser parser; + llama_grammar_parser parser(vocab); // if there is a grammar, parse it // rules will be empty (default) if there are parse errors @@ -1077,10 +1204,11 @@ struct llama_grammar * llama_grammar_init_impl( vocab, std::move(vec_rules), std::move(stacks), - /* .partial_utf8 = */ {}, - /* .lazy = */ lazy, - /* .awaiting_trigger = */ lazy, - /* .trigger_buffer = */ "", + /* .partial_utf8 = */ {}, + /* .lazy = */ lazy, + /* .awaiting_trigger = */ lazy, + /* .trigger_buffer = */ "", + /* .trigger_buffer_positions = */ {}, std::move(vec_trigger_tokens), std::move(vec_trigger_patterns), }; @@ -1103,6 +1231,7 @@ struct llama_grammar * llama_grammar_clone_impl(const struct llama_grammar & gra grammar.lazy, grammar.awaiting_trigger, grammar.trigger_buffer, + grammar.trigger_buffer_positions, grammar.trigger_tokens, grammar.trigger_patterns, }; @@ -1156,7 +1285,7 @@ void llama_grammar_apply_impl(const struct llama_grammar & grammar, llama_token_ cur_p->data[i].logit = -INFINITY; } else { candidates_decoded.push_back(decode_utf8(piece, grammar.partial_utf8)); - candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second }); + candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second, id }); } } @@ -1175,10 +1304,12 @@ void llama_grammar_accept_impl(struct llama_grammar & grammar, llama_token token if (std::find(grammar.trigger_tokens.begin(), grammar.trigger_tokens.end(), token) != grammar.trigger_tokens.end()) { grammar.awaiting_trigger = false; grammar.trigger_buffer.clear(); - llama_grammar_accept_str(grammar, piece); + llama_grammar_accept_token(grammar, token, piece); LLAMA_LOG_DEBUG("Grammar triggered on token %u (`%s`)", token, piece.c_str()); return; } else { + auto position = std::make_pair(grammar.trigger_buffer.size(), grammar.trigger_buffer.size() + piece.size()); + grammar.trigger_buffer_positions.push_back(std::make_pair(token, position)); grammar.trigger_buffer += piece; std::smatch match; @@ -1196,10 +1327,23 @@ void llama_grammar_accept_impl(struct llama_grammar & grammar, llama_token token if (start == std::string::npos) { start = match.position(0); } + + // replay tokens that overlap with [start, end) + for (const auto & [tok, tok_pos] : grammar.trigger_buffer_positions) { + auto [tok_start, tok_end] = tok_pos; + if (tok_end <= start) { + continue; + } + + size_t piece_start = (tok_start < start) ? start : tok_start; // allow for partial token pieces + size_t piece_len = tok_end - piece_start; + auto tok_piece = grammar.trigger_buffer.substr(piece_start, piece_len); + llama_grammar_accept_token(grammar, tok, tok_piece); + } + auto constrained_str = grammar.trigger_buffer.substr(start); - // std::string constrained_str(match[1].first, grammar.trigger_buffer.end()); grammar.trigger_buffer.clear(); - llama_grammar_accept_str(grammar, constrained_str); + grammar.trigger_buffer_positions.clear(); LLAMA_LOG_DEBUG("Grammar triggered on regex: '%s'\n", constrained_str.c_str()); return; } @@ -1218,7 +1362,7 @@ void llama_grammar_accept_impl(struct llama_grammar & grammar, llama_token token GGML_ABORT("fatal error"); } - llama_grammar_accept_str(grammar, piece); + llama_grammar_accept_token(grammar, token, piece); } void llama_grammar_accept_str(struct llama_grammar & grammar, const std::string & piece) { @@ -1235,3 +1379,59 @@ void llama_grammar_accept_str(struct llama_grammar & grammar, const std::string throw std::runtime_error("Unexpected empty grammar stack after accepting piece: " + piece); } } + +void llama_grammar_accept_token(struct llama_grammar & grammar, llama_token token, const std::string & piece) { + // Note terminating 0 in decoded string + const auto decoded = decode_utf8(piece, grammar.partial_utf8); + const auto & code_points = decoded.first; + + llama_grammar_stacks stacks_new; + stacks_new.reserve(grammar.stacks.size()); + + for (const auto & stack : grammar.stacks) { + if (stack.empty()) { + continue; + } + + const llama_grammar_element * pos = stack.back(); + + if (pos->type == LLAMA_GRETYPE_TOKEN || pos->type == LLAMA_GRETYPE_TOKEN_NOT) { + if (llama_grammar_match_token(pos, token)) { + llama_grammar_stack new_stack(stack.begin(), stack.end() - 1); + if (!llama_grammar_is_end_of_sequence(pos + 1)) { + new_stack.push_back(pos + 1); + } + llama_grammar_advance_stack(grammar.rules, new_stack, stacks_new); + } + } else { + llama_grammar_stacks current_stacks = {stack}; + + for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) { + llama_grammar_stacks next_stacks; + + for (const auto & cur_stack : current_stacks) { + llama_grammar_accept_chr(grammar, cur_stack, *it, next_stacks); + } + + current_stacks = std::move(next_stacks); + if (current_stacks.empty()) { + break; + } + } + + for (auto & surviving_stack : current_stacks) { + if (std::find(stacks_new.begin(), stacks_new.end(), surviving_stack) == stacks_new.end()) { + stacks_new.emplace_back(surviving_stack); + } + } + } + } + + grammar.stacks = std::move(stacks_new); + grammar.partial_utf8 = decoded.second; + + if (grammar.stacks.empty()) { + throw std::runtime_error("Unexpected empty grammar stack after accepting piece: " + piece + " (" + std::to_string(token) + ")"); + } +} + diff --git a/src/llama-grammar.h b/src/llama-grammar.h index f8c291de99..a4c978ac11 100644 --- a/src/llama-grammar.h +++ b/src/llama-grammar.h @@ -36,11 +36,17 @@ enum llama_gretype { // any character (.) LLAMA_GRETYPE_CHAR_ANY = 7, + + // terminal element: token (<[token-id]>) + LLAMA_GRETYPE_TOKEN = 8, + + // inverse token (!<[token-id]>) + LLAMA_GRETYPE_TOKEN_NOT = 9, }; typedef struct llama_grammar_element { enum llama_gretype type; - uint32_t value; // Unicode code point or rule ID + uint32_t value; // Unicode code point, rule ID, or token ID } llama_grammar_element; struct llama_partial_utf8 { @@ -52,6 +58,7 @@ struct llama_grammar_candidate { size_t index; const uint32_t * code_points; llama_partial_utf8 partial_utf8; + llama_token id; }; using llama_grammar_rule = std::vector< llama_grammar_element>; @@ -77,10 +84,13 @@ std::vector llama_grammar_reject_candidates_for_stack( const llama_grammar_candidates & candidates); struct llama_grammar_parser { + const llama_vocab * vocab; std::map symbol_ids; llama_grammar_rules rules; + llama_grammar_parser(const struct llama_vocab * vocab = nullptr) : vocab(vocab) {} + llama_grammar_stack c_rules() const; uint32_t get_symbol_id(const char * src, size_t len); @@ -112,6 +122,9 @@ struct llama_grammar_trigger_pattern { }; struct llama_grammar { + // maintain a list of llama_tokens and their positions in the trigger_buffer + using token_pos = std::pair>; + // note: allow null vocab for testing (not great) const llama_vocab * vocab; @@ -127,6 +140,7 @@ struct llama_grammar { bool lazy = false; bool awaiting_trigger = false; // Initialized to true for lazy grammars only std::string trigger_buffer; // Output buffered by lazy grammar. Will be cleared once trigger is found. + std::vector trigger_buffer_positions; // Tokens buffered by lazy grammar. Used to replay when a trigger is found. std::vector trigger_tokens; // Tokens that trigger a lazy grammar, or tokens to force printing of (even if special). std::vector trigger_patterns; // Regular expressions that trigger a lazy grammar. Must be a full match of the entire generated @@ -171,3 +185,8 @@ void llama_grammar_accept_impl( void llama_grammar_accept_str( struct llama_grammar & grammar, const std::string & piece); + +void llama_grammar_accept_token( + struct llama_grammar & grammar, + llama_token token, + const std::string & piece); diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index 43620df780..8909bbfb95 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -78,7 +78,7 @@ void llm_graph_input_attn_temp::set_input(const llama_ubatch * ubatch) { for (int i = 0; i < n_tokens; ++i) { const float pos = ubatch->pos[i]; attn_scale_data[i] = std::log( - std::floor((pos + 1.0f) / n_attn_temp_floor_scale) + 1.0 + std::floor((pos + f_attn_temp_offset) / n_attn_temp_floor_scale) + 1.0 ) * f_attn_temp_scale + 1.0; } @@ -385,7 +385,7 @@ bool llm_graph_input_attn_kv::can_reuse(const llm_graph_params & params) { //res &= self_v_idxs->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there res &= self_kq_mask->ne[0] == mctx->get_n_kv(); - res &= self_kq_mask->ne[1] == GGML_PAD(params.ubatch.n_tokens, GGML_KQ_MASK_PAD); + res &= self_kq_mask->ne[1] == params.ubatch.n_tokens; return res; } @@ -416,10 +416,10 @@ bool llm_graph_input_attn_kv_iswa::can_reuse(const llm_graph_params & params) { //res &= self_v_idxs_swa->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there res &= self_kq_mask->ne[0] == mctx->get_base()->get_n_kv(); - res &= self_kq_mask->ne[1] == GGML_PAD(params.ubatch.n_tokens, GGML_KQ_MASK_PAD); + res &= self_kq_mask->ne[1] == params.ubatch.n_tokens; res &= self_kq_mask_swa->ne[0] == mctx->get_swa()->get_n_kv(); - res &= self_kq_mask_swa->ne[1] == GGML_PAD(params.ubatch.n_tokens, GGML_KQ_MASK_PAD); + res &= self_kq_mask_swa->ne[1] == params.ubatch.n_tokens; return res; } @@ -452,7 +452,7 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) { } } - for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) { + for (int i = n_tokens; i < n_tokens; ++i) { for (int j = 0; j < n_enc; ++j) { data[h*(n_enc*n_tokens) + i*n_enc + j] = -INFINITY; } @@ -1203,7 +1203,7 @@ ggml_tensor * llm_graph_context::build_inp_pos() const { } ggml_tensor * llm_graph_context::build_inp_attn_scale() const { - auto inp = std::make_unique(hparams.n_attn_temp_floor_scale, hparams.f_attn_temp_scale); + auto inp = std::make_unique(hparams.n_attn_temp_floor_scale, hparams.f_attn_temp_scale, hparams.f_attn_temp_offset); auto & cur = inp->attn_scale; @@ -1470,13 +1470,13 @@ llm_graph_input_attn_no_cache * llm_graph_context::build_attn_inp_no_cache() con auto inp = std::make_unique(hparams, cparams); // note: there is no KV cache, so the number of KV values is equal to the number of tokens in the batch - inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1, 1); + inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens, 1, 1); ggml_set_input(inp->self_kq_mask); inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask; if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) { - inp->self_kq_mask_swa = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1, 1); + inp->self_kq_mask_swa = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens, 1, 1); ggml_set_input(inp->self_kq_mask_swa); inp->self_kq_mask_swa_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask_swa, GGML_TYPE_F16) : inp->self_kq_mask_swa; @@ -1558,7 +1558,7 @@ static std::unique_ptr build_attn_inp_kv_impl( inp->self_k_idxs = mctx_cur->build_input_k_idxs(ctx0, ubatch); inp->self_v_idxs = mctx_cur->build_input_v_idxs(ctx0, ubatch); - inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens/n_stream, GGML_KQ_MASK_PAD), 1, n_stream); + inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, n_tokens/n_stream, 1, n_stream); ggml_set_input(inp->self_kq_mask); inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask; @@ -1701,7 +1701,7 @@ llm_graph_input_attn_cross * llm_graph_context::build_attn_inp_cross() const { const int32_t n_enc = !cross->v_embd.empty() ? cross->n_enc : hparams.n_ctx_train; - inp->cross_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_enc, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1, 1); + inp->cross_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_enc, n_tokens, 1, 1); ggml_set_input(inp->cross_kq_mask); inp->cross_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->cross_kq_mask, GGML_TYPE_F16) : inp->cross_kq_mask; @@ -1767,7 +1767,7 @@ llm_graph_input_attn_kv_iswa * llm_graph_context::build_attn_inp_kv_iswa() const inp->self_k_idxs = mctx_cur->get_base()->build_input_k_idxs(ctx0, ubatch); inp->self_v_idxs = mctx_cur->get_base()->build_input_v_idxs(ctx0, ubatch); - inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens/n_stream, GGML_KQ_MASK_PAD), 1, n_stream); + inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, n_tokens/n_stream, 1, n_stream); ggml_set_input(inp->self_kq_mask); inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask; @@ -1781,7 +1781,7 @@ llm_graph_input_attn_kv_iswa * llm_graph_context::build_attn_inp_kv_iswa() const inp->self_k_idxs_swa = mctx_cur->get_swa()->build_input_k_idxs(ctx0, ubatch); inp->self_v_idxs_swa = mctx_cur->get_swa()->build_input_v_idxs(ctx0, ubatch); - inp->self_kq_mask_swa = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens/n_stream, GGML_KQ_MASK_PAD), 1, n_stream); + inp->self_kq_mask_swa = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, n_tokens/n_stream, 1, n_stream); ggml_set_input(inp->self_kq_mask_swa); inp->self_kq_mask_swa_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask_swa, GGML_TYPE_F16) : inp->self_kq_mask_swa; diff --git a/src/llama-graph.h b/src/llama-graph.h index d0c3934f67..e9d387bd7c 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -132,8 +132,8 @@ public: // temperature tuning, used by llama4 class llm_graph_input_attn_temp : public llm_graph_input_i { public: - llm_graph_input_attn_temp(uint32_t n_attn_temp_floor_scale, float f_attn_temp_scale) - : n_attn_temp_floor_scale(n_attn_temp_floor_scale), f_attn_temp_scale(f_attn_temp_scale) {} + llm_graph_input_attn_temp(uint32_t n_attn_temp_floor_scale, float f_attn_temp_scale, float f_attn_temp_offset) + : n_attn_temp_floor_scale(n_attn_temp_floor_scale), f_attn_temp_scale(f_attn_temp_scale), f_attn_temp_offset(f_attn_temp_offset) {} virtual ~llm_graph_input_attn_temp() = default; void set_input(const llama_ubatch * ubatch) override; @@ -142,6 +142,7 @@ public: const uint32_t n_attn_temp_floor_scale; const float f_attn_temp_scale; + const float f_attn_temp_offset; }; class llm_graph_input_pos_bucket : public llm_graph_input_i { diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp index 8cdbaf69fc..96c9598c24 100644 --- a/src/llama-hparams.cpp +++ b/src/llama-hparams.cpp @@ -1,6 +1,7 @@ #include "llama-hparams.h" #include "ggml.h" + #include void llama_hparams::set_swa_pattern(uint32_t n_pattern, bool dense_first) { diff --git a/src/llama-hparams.h b/src/llama-hparams.h index 6eff334a5f..cecb476e91 100644 --- a/src/llama-hparams.h +++ b/src/llama-hparams.h @@ -34,6 +34,7 @@ struct llama_hparams_convnext { struct llama_hparams { bool vocab_only; + bool no_alloc; bool rope_finetuned; bool use_par_res; bool swin_norm; @@ -107,6 +108,7 @@ struct llama_hparams { float rope_freq_base_train_swa; float rope_freq_scale_train; float rope_freq_scale_train_swa; + uint32_t n_ctx_orig_yarn; float rope_yarn_log_mul = 0.0f; @@ -164,6 +166,7 @@ struct llama_hparams { uint32_t n_no_rope_layer_step = 4; uint32_t n_attn_temp_floor_scale = 0; float f_attn_temp_scale = 0.0f; + float f_attn_temp_offset = 0.0f; // offset position index // gemma3n altup uint32_t n_altup = 4; // altup_num_inputs @@ -270,4 +273,3 @@ struct llama_hparams { }; static_assert(std::is_trivially_copyable::value, "llama_hparams must be trivially copyable"); - diff --git a/src/llama-impl.cpp b/src/llama-impl.cpp index c7a1880aad..8e3e7b223a 100644 --- a/src/llama-impl.cpp +++ b/src/llama-impl.cpp @@ -25,6 +25,10 @@ time_meas::~time_meas() { } } +void llama_log_get(ggml_log_callback * log_callback, void ** user_data) { + ggml_log_get(log_callback, user_data); +} + void llama_log_set(ggml_log_callback log_callback, void * user_data) { ggml_log_set(log_callback, user_data); g_logger_state.log_callback = log_callback ? log_callback : llama_log_callback_default; diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp index e26385a1fe..3186242d60 100644 --- a/src/llama-kv-cache.cpp +++ b/src/llama-kv-cache.cpp @@ -175,7 +175,15 @@ llama_kv_cache::llama_kv_cache( // allocate tensors and initialize the buffers to avoid NaNs in the padding for (auto & [buft, ctx] : ctx_map) { - ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx.get(), buft); + ggml_backend_buffer_t buf; + if (model.hparams.no_alloc) { + buf = ggml_backend_buft_alloc_buffer(buft, /*size =*/ 0); // dummy buffer + for (ggml_tensor * t = ggml_get_first_tensor(ctx.get()); t != nullptr; t = ggml_get_next_tensor(ctx.get(), t)) { + t->buffer = buf; // set dummy buffer for KV cache so that the backend scheduler won't try to allocate it + } + } else { + buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx.get(), buft); // real buffer + } if (!buf) { throw std::runtime_error("failed to allocate buffer for kv cache"); } @@ -482,9 +490,18 @@ llama_pos llama_kv_cache::seq_pos_max(llama_seq_id seq_id) const { std::map llama_kv_cache::memory_breakdown() const { std::map ret; - for (const auto & [_, buf] : ctxs_bufs) { - ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get()); + for (const auto & [ctx, buf] : ctxs_bufs) { + ggml_backend_buffer_type_t buft = ggml_backend_buffer_get_type(buf.get()); + + if (hparams.no_alloc) { + GGML_ASSERT(ggml_backend_buffer_get_base(buf.get()) == nullptr); + ret[buft] += ggml_backend_alloc_ctx_tensors_from_buft_size(ctx.get(), buft); + } else { + // GGML_ASSERT(ggml_backend_buffer_get_base(buf.get()) != nullptr); // multi_buffer does not have a defined base + ret[buft] += ggml_backend_buffer_get_size(buf.get()); + } } + return ret; } @@ -1232,8 +1249,7 @@ void llama_kv_cache::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * u GGML_ASSERT(n_tokens%n_stream == 0); // n_tps == n_tokens_per_stream - const int64_t n_tps = n_tokens/n_stream; - const int64_t n_tps_pad = GGML_PAD(n_tps, GGML_KQ_MASK_PAD); + const int64_t n_tps = n_tokens/n_stream; std::fill(data, data + ggml_nelements(dst), -INFINITY); @@ -1266,7 +1282,7 @@ void llama_kv_cache::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * u const llama_pos p1_x = is_2d ? ubatch->pos[i + ubatch->n_tokens*2] : 0; const llama_pos p1_y = is_2d ? ubatch->pos[i + ubatch->n_tokens] : 0; - const uint64_t idst = n_kv*(h*n_stream*n_tps_pad + s*n_tps_pad + ii); + const uint64_t idst = n_kv*(h*n_stream*n_tps + s*n_tps + ii); for (uint32_t j = 0; j < n_kv; ++j) { if (cells.is_empty(j)) { @@ -1370,9 +1386,10 @@ ggml_tensor * llama_kv_cache::build_rope_shift( float freq_scale) const { const auto & n_ctx_orig = cparams.n_ctx_orig_yarn; - const auto & yarn_ext_factor = cparams.yarn_ext_factor; - const auto & yarn_beta_fast = cparams.yarn_beta_fast; - const auto & yarn_beta_slow = cparams.yarn_beta_slow; + const auto & yarn_ext_factor = cparams.yarn_ext_factor; + const auto & yarn_beta_fast = cparams.yarn_beta_fast; + const auto & yarn_beta_slow = cparams.yarn_beta_slow; + const auto & yarn_attn_factor = cparams.yarn_attn_factor; const auto & n_rot = hparams.n_rot; const auto & rope_type = hparams.rope_type == LLAMA_ROPE_TYPE_MROPE || hparams.rope_type == LLAMA_ROPE_TYPE_IMROPE @@ -1383,12 +1400,6 @@ ggml_tensor * llama_kv_cache::build_rope_shift( ? LLAMA_ROPE_TYPE_NEOX : hparams.rope_type; - // See llm_build_deepseek2() for why attn_factor has to be scaled for YaRN RoPE to work correctly. - // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation. - const float yarn_attn_factor = model.arch == LLM_ARCH_DEEPSEEK2 - ? 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale)) - : cparams.yarn_attn_factor; - ggml_tensor * tmp; if (ggml_is_quantized(cur->type)) { @@ -1550,9 +1561,11 @@ void llama_kv_cache::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama const uint32_t strm = seq_id == -1 ? s : seq_to_stream[seq_id]; + slot_info sinfo; + bool res = true; - res = res && state_read_meta(io, strm, cell_count, seq_id); - res = res && state_read_data(io, strm, cell_count); + res = res && state_read_meta(io, strm, cell_count, sinfo, seq_id); + res = res && state_read_data(io, strm, cell_count, sinfo); if (!res) { if (seq_id == -1) { @@ -1691,7 +1704,7 @@ void llama_kv_cache::state_write_data(llama_io_write_i & io, const cell_ranges_t } } -bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, llama_seq_id dest_seq_id) { +bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, slot_info & sinfo, llama_seq_id dest_seq_id) { auto & cells = v_cells[strm]; auto & head = v_heads[strm]; @@ -1728,7 +1741,7 @@ bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32 ubatch.seq_id[i] = &dest_seq_id; } - const auto sinfo = find_slot(ubatch, true); + sinfo = find_slot(ubatch, false); if (sinfo.empty()) { LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__); return false; @@ -1738,20 +1751,16 @@ bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32 // see: https://github.com/ggml-org/llama.cpp/pull/16825#issuecomment-3460868350 apply_ubatch(sinfo, ubatch); - const auto head_cur = sinfo.head(); + LLAMA_LOG_DEBUG("%s: cell_count = %d, dest_seq_id = %d\n", __func__, cell_count, dest_seq_id); - // keep the head at the old position because we will read the KV data into it in state_read_data() - head = head_cur; - - LLAMA_LOG_DEBUG("%s: head_cur = %d, head = %d, cell_count = %d, dest_seq_id = %d\n", __func__, head_cur, head, cell_count, dest_seq_id); - - // DEBUG CHECK: head_cur should be our first cell, head_cur + cell_count - 1 should be our last cell (verify seq_id and pos values) - // Assume that this is one contiguous block of cells - GGML_ASSERT(head_cur + cell_count <= cells.size()); - GGML_ASSERT(cells.pos_get(head_cur) == ubatch.pos[0]); - GGML_ASSERT(cells.pos_get(head_cur + cell_count - 1) == ubatch.pos[cell_count - 1]); - GGML_ASSERT(cells.seq_has(head_cur, dest_seq_id)); - GGML_ASSERT(cells.seq_has(head_cur + cell_count - 1, dest_seq_id)); + // DEBUG CHECK: verify that all cells were allocated and have correct seq_id and pos values + GGML_ASSERT(sinfo.n_stream() == 1); + GGML_ASSERT(sinfo.idxs[0].size() == cell_count); + for (uint32_t i = 0; i < cell_count; ++i) { + const uint32_t idx = sinfo.idxs[0][i]; + GGML_ASSERT(cells.pos_get(idx) == ubatch.pos[i]); + GGML_ASSERT(cells.seq_has(idx, dest_seq_id)); + } } else { // whole KV cache restore @@ -1784,15 +1793,24 @@ bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32 } } + // Create contiguous slot_info for whole cache restore + sinfo.s0 = strm; + sinfo.s1 = strm; + sinfo.resize(1); + sinfo.strm[0] = strm; + sinfo.idxs[0].resize(cell_count); + for (uint32_t i = 0; i < cell_count; ++i) { + sinfo.idxs[0][i] = i; + } + head = 0; } return true; } -bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count) { +bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, const slot_info & sinfo) { auto & cells = v_cells[strm]; - auto & head = v_heads[strm]; uint32_t v_trans; uint32_t n_layer; @@ -1842,8 +1860,17 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32 } if (cell_count) { - // Read and set the keys for the whole cell range - ggml_backend_tensor_set(k, io.read(cell_count * k_size_row), head * k_size_row, cell_count * k_size_row); + if (sinfo.is_contiguous()) { + // Fast path: contiguous cells, single memcpy + ggml_backend_tensor_set(k, io.read(cell_count * k_size_row), sinfo.head() * k_size_row, cell_count * k_size_row); + } else { + // Slow path: scatter to non-contiguous positions + const void * src = io.read(cell_count * k_size_row); + for (uint32_t i = 0; i < cell_count; ++i) { + const size_t dst_offset = sinfo.idxs[0][i] * k_size_row; + ggml_backend_tensor_set(k, (const char*)src + i * k_size_row, dst_offset, k_size_row); + } + } } } @@ -1874,8 +1901,17 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32 } if (cell_count) { - // Read and set the values for the whole cell range - ggml_backend_tensor_set(v, io.read(cell_count * v_size_row), head * v_size_row, cell_count * v_size_row); + if (sinfo.is_contiguous()) { + // Fast path: contiguous cells, single memcpy + ggml_backend_tensor_set(v, io.read(cell_count * v_size_row), sinfo.head() * v_size_row, cell_count * v_size_row); + } else { + // Slow path: scatter to non-contiguous positions + const void * src = io.read(cell_count * v_size_row); + for (uint32_t i = 0; i < cell_count; ++i) { + const size_t dst_offset = sinfo.idxs[0][i] * v_size_row; + ggml_backend_tensor_set(v, (const char*)src + i * v_size_row, dst_offset, v_size_row); + } + } } } } else { @@ -1914,10 +1950,22 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32 } if (cell_count) { - // For each row in the transposed matrix, read the values for the whole cell range - for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { - const size_t dst_offset = (head + j * cells.size()) * v_size_el; - ggml_backend_tensor_set(v, io.read(cell_count * v_size_el), dst_offset, cell_count * v_size_el); + if (sinfo.is_contiguous()) { + // Fast path: contiguous cells + const uint32_t h = sinfo.head(); + for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { + const size_t dst_offset = (h + j * cells.size()) * v_size_el; + ggml_backend_tensor_set(v, io.read(cell_count * v_size_el), dst_offset, cell_count * v_size_el); + } + } else { + // Slow path: scatter to non-contiguous positions + for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { + const void * src = io.read(cell_count * v_size_el); + for (uint32_t i = 0; i < cell_count; ++i) { + const size_t dst_offset = (sinfo.idxs[0][i] + j * cells.size()) * v_size_el; + ggml_backend_tensor_set(v, (const char*)src + i * v_size_el, dst_offset, v_size_el); + } + } } } } diff --git a/src/llama-kv-cache.h b/src/llama-kv-cache.h index bf7821c07c..1868f11857 100644 --- a/src/llama-kv-cache.h +++ b/src/llama-kv-cache.h @@ -72,6 +72,23 @@ public: void clear() { idxs.clear(); } + + // check if indices are contiguous starting from head() + bool is_contiguous() const { + if (idxs.empty() || idxs[0].empty()) { + return true; + } + if (idxs.size() > 1) { + return false; + } + const uint32_t h = idxs[0][0]; + for (size_t i = 0; i < idxs[0].size(); ++i) { + if (idxs[0][i] != h + i) { + return false; + } + } + return true; + } }; using slot_info_vec_t = std::vector; @@ -264,8 +281,8 @@ private: void state_write_meta(llama_io_write_i & io, const cell_ranges_t & cr, llama_seq_id seq_id = -1) const; void state_write_data(llama_io_write_i & io, const cell_ranges_t & cr) const; - bool state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, llama_seq_id dest_seq_id = -1); - bool state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count); + bool state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, slot_info & sinfo, llama_seq_id dest_seq_id = -1); + bool state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, const slot_info & sinfo); }; class llama_kv_cache_context : public llama_memory_context_i { diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp index aa3a65f87a..ca2ea2461d 100644 --- a/src/llama-model-loader.cpp +++ b/src/llama-model-loader.cpp @@ -473,6 +473,7 @@ llama_model_loader::llama_model_loader( std::vector & splits, bool use_mmap, bool check_tensors, + bool no_alloc, const llama_model_kv_override * param_overrides_p, const llama_model_tensor_buft_override * param_tensor_buft_overrides_p) { int trace = 0; @@ -716,6 +717,7 @@ llama_model_loader::llama_model_loader( this->use_mmap = use_mmap; this->check_tensors = check_tensors; + this->no_alloc = no_alloc; } std::string llama_model_loader::get_arch_name() const { diff --git a/src/llama-model-loader.h b/src/llama-model-loader.h index c9189f6cb4..0380c92fde 100644 --- a/src/llama-model-loader.h +++ b/src/llama-model-loader.h @@ -71,6 +71,7 @@ struct llama_model_loader { bool use_mmap = false; bool check_tensors; + bool no_alloc; llama_files files; llama_ftype ftype; @@ -97,6 +98,7 @@ struct llama_model_loader { std::vector & splits, // optional, only need if the split does not follow naming scheme bool use_mmap, bool check_tensors, + bool no_alloc, const llama_model_kv_override * param_overrides_p, const llama_model_tensor_buft_override * param_tensor_buft_overrides_p); diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 7d09d7abd5..050735afc0 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -120,6 +120,7 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_16B_A1B: return "16B.A1B"; case LLM_TYPE_21B_A3B: return "21B.A3B"; case LLM_TYPE_30B_A3B: return "30B.A3B"; + case LLM_TYPE_80B_A3B: return "80B.A3B"; case LLM_TYPE_100B_A6B: return "100B.A6B"; case LLM_TYPE_106B_A12B: return "106B.A12B"; case LLM_TYPE_230B_A10B: return "230B.A10B"; @@ -667,6 +668,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { hparams.n_swa = 8192; hparams.n_attn_temp_floor_scale = 8192; hparams.f_attn_temp_scale = 0.1f; + hparams.f_attn_temp_offset = 1.0f; hparams.set_swa_pattern(4); // pattern: 3 chunked - 1 full } @@ -1264,18 +1266,25 @@ void llama_model::load_hparams(llama_model_loader & ml) { } break; case LLM_ARCH_GEMMA3: { - hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; - hparams.set_swa_pattern(6); + const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false); + if (found_swa && hparams.n_swa > 0) { + hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; + hparams.set_swa_pattern(6); - hparams.rope_freq_base_train_swa = 10000.0f; - hparams.rope_freq_scale_train_swa = 1.0f; + hparams.rope_freq_base_train_swa = 10000.0f; + hparams.rope_freq_scale_train_swa = 1.0f; + } else { + hparams.swa_type = LLAMA_SWA_TYPE_NONE; + } - ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); + hparams.f_final_logit_softcapping = 0.0f; + ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false); ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); switch (hparams.n_layer) { case 18: type = LLM_TYPE_270M; break; case 26: type = LLM_TYPE_1B; break; + case 32: type = LLM_TYPE_8B; break; // Rnj-1 case 34: type = LLM_TYPE_4B; break; case 48: type = LLM_TYPE_12B; break; case 62: type = LLM_TYPE_27B; break; @@ -1599,8 +1608,9 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale); - switch (hparams.n_layer) { - case 28: type = LLM_TYPE_20B; break; + switch (hparams.n_ff_exp) { + case 1408: type = LLM_TYPE_16B; break; + case 1792: type = LLM_TYPE_20B; break; default: type = LLM_TYPE_UNKNOWN; } } break; @@ -1626,12 +1636,19 @@ void llama_model::load_hparams(llama_model_loader & ml) { // that have no expert_gating_func model parameter set hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX; } - ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, false); + + if (ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, 0.0f)) { + // [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX] + // cancel the factor from the convert script + hparams.rope_yarn_log_mul /= 0.1f; + } // (optional) temperature tuning - used by mistral-large ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false); ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.n_attn_temp_floor_scale, false); + hparams.f_attn_temp_offset = 0.0f; + switch (hparams.n_layer) { case 27: type = LLM_TYPE_16B; break; case 60: type = LLM_TYPE_236B; break; @@ -2249,7 +2266,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { } switch (hparams.n_layer) { - case 80: type = LLM_TYPE_80B_A3B; break; + case 48: type = LLM_TYPE_80B_A3B; break; default: type = LLM_TYPE_UNKNOWN; } } break; @@ -2258,9 +2275,11 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false); - ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST, hparams.yarn_beta_fast, false); - ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, hparams.yarn_beta_slow, false); - ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, false); + ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST, hparams.yarn_beta_fast, false); + ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, hparams.yarn_beta_slow, false); + ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, 0.0f); + + hparams.f_attn_temp_offset = 0.0f; // TODO: maybe add n_attn_temp_floor_scale as a separate KV? if (hparams.f_attn_temp_scale != 0.0f) { @@ -2270,18 +2289,6 @@ void llama_model::load_hparams(llama_model_loader & ml) { } } - // TODO: this seems to be correct with the case of mscale == mscale_all_dims == 1.0f - // but may need further verification with other values - if (hparams.rope_yarn_log_mul != 0.0f) { - float factor = 1.0f / hparams.rope_freq_scale_train; - float mscale = 1.0f; - float mscale_all_dims = hparams.rope_yarn_log_mul; - static auto get_mscale = [](float scale, float mscale) { - return scale <= 1.0f ? 1.0f : (0.1f * mscale * logf(scale) + 1.0f); - }; - hparams.yarn_attn_factor = get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dims); - } - switch (hparams.n_layer) { case 26: type = LLM_TYPE_3B; break; case 34: type = LLM_TYPE_8B; break; @@ -3381,9 +3388,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) { layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); // optional bias tensors - layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0); - layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0); - layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED); layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); @@ -6599,9 +6606,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) { std::vector bufs; if (ml.use_mmap && use_mmap_buffer && buffer_from_host_ptr_supported && is_default_buft) { + GGML_ASSERT(!ml.no_alloc); for (uint32_t idx = 0; idx < ml.files.size(); idx++) { // only the mmap region containing the tensors in the model is mapped to the backend buffer - // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers + // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, + // then we could just use metal for all layers // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size void * addr = nullptr; size_t first, last; // NOLINT @@ -6617,9 +6626,16 @@ bool llama_model::load_tensors(llama_model_loader & ml) { bufs.emplace_back(buf); buf_map.emplace(idx, buf); } - } - else { - ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); + } else { + ggml_backend_buffer_t buf; + if (ml.no_alloc) { + buf = ggml_backend_buft_alloc_buffer(buft, /*size =*/ 0); // dummy buffer + for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) { + t->buffer = buf; // set dummy buffer for weights so that the backend scheduler won't try to allocate them + } + } else { + buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); // real buffer + } if (buf == nullptr) { throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft))); } @@ -6674,6 +6690,10 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } } + if (ml.no_alloc) { + return true; + } + // load tensor data for (auto & [ctx, buf_map] : ctx_buf_maps) { if (!ml.load_all_data(ctx, buf_map, use_mlock ? &pimpl->mlock_mmaps : NULL, params.progress_callback, params.progress_callback_user_data)) { @@ -6716,9 +6736,18 @@ size_t llama_model::n_devices() const { std::map llama_model::memory_breakdown() const { std::map ret; - for (const auto & [_, bufs] : pimpl->ctxs_bufs) { - for (const auto & buf : bufs) { - ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get()); + for (const auto & [ctx, bufs] : pimpl->ctxs_bufs) { + if (hparams.no_alloc) { + GGML_ASSERT(bufs.size() == 1); + ggml_backend_buffer_t buf = bufs[0].get(); + GGML_ASSERT(ggml_backend_buffer_get_base(buf) == nullptr); + ggml_backend_buffer_type_t buft = ggml_backend_buffer_get_type(buf); + ret[buft] += ggml_backend_alloc_ctx_tensors_from_buft_size(ctx.get(), buft); + } else { + for (const auto & buf : bufs) { + // GGML_ASSERT(ggml_backend_buffer_get_base(buf.get()) != nullptr); // multi_buffer does not have a defined base + ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get()); + } } } return ret; @@ -6763,6 +6792,7 @@ void llama_model::print_info() const { // hparams LLAMA_LOG_INFO("%s: arch = %s\n", __func__, arch_name().c_str()); LLAMA_LOG_INFO("%s: vocab_only = %d\n", __func__, hparams.vocab_only); + LLAMA_LOG_INFO("%s: no_alloc = %d\n", __func__, hparams.no_alloc); if (!hparams.vocab_only) { LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train); @@ -6797,6 +6827,7 @@ void llama_model::print_info() const { LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train); LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train); LLAMA_LOG_INFO("%s: n_ctx_orig_yarn = %u\n", __func__, hparams.n_ctx_orig_yarn); + LLAMA_LOG_INFO("%s: rope_yarn_log_mul= %.4f\n", __func__, hparams.rope_yarn_log_mul); LLAMA_LOG_INFO("%s: rope_finetuned = %s\n", __func__, hparams.rope_finetuned ? "yes" : "unknown"); // MRoPE (Multi-axis Rotary Position Embedding) sections if (const auto & s = hparams.rope_sections; s[0] || s[1] || s[2] || s[3]) { @@ -6860,7 +6891,6 @@ void llama_model::print_info() const { LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale); LLAMA_LOG_INFO("%s: expert_weights_norm = %d\n", __func__, hparams.expert_weights_norm); LLAMA_LOG_INFO("%s: expert_gating_func = %s\n", __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func)); - LLAMA_LOG_INFO("%s: rope_yarn_log_mul = %.4f\n", __func__, hparams.rope_yarn_log_mul); } if (arch == LLM_ARCH_QWEN2MOE) { @@ -7304,7 +7334,11 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const { } break; case LLM_ARCH_GEMMA3: { - llm = std::make_unique(*this, params); + if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) { + llm = std::make_unique>(*this, params); + } else { + llm = std::make_unique>(*this, params); + } } break; case LLM_ARCH_GEMMA3N: { @@ -7607,6 +7641,7 @@ llama_model_params llama_model_default_params() { /*.check_tensors =*/ false, /*.use_extra_bufts =*/ true, /*.no_host =*/ false, + /*.no_alloc =*/ false, }; return result; diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp index 351dcb7baa..bc4b05c3b5 100644 --- a/src/llama-quant.cpp +++ b/src/llama-quant.cpp @@ -596,7 +596,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: } std::vector splits = {}; - llama_model_loader ml(fname_inp, splits, use_mmap, /*check_tensors*/ true, kv_overrides, nullptr); + llama_model_loader ml(fname_inp, splits, use_mmap, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr); ml.init_mappings(false); // no prefetching llama_model model(llama_model_default_params()); diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index e2cca66e48..7b01a2edfe 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -1895,7 +1895,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { clean_spaces = false; } else if ( tokenizer_pre == "qwen2" || - tokenizer_pre == "deepseek-r1-qwen") { + tokenizer_pre == "deepseek-r1-qwen" || + tokenizer_pre == "kormo") { pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2; clean_spaces = false; } else if ( diff --git a/src/llama.cpp b/src/llama.cpp index ab2e9868af..7ed34b80ae 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -1,6 +1,9 @@ +#include "llama.h" + #include "llama-impl.h" #include "llama-chat.h" +#include "llama-context.h" #include "llama-mmap.h" #include "llama-vocab.h" #include "llama-model-loader.h" @@ -11,11 +14,14 @@ #include "ggml-backend.h" #include +#include +#include #include #include #include #include #include +#include #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data @@ -37,6 +43,643 @@ const char * llama_flash_attn_type_name(enum llama_flash_attn_type flash_attn_ty GGML_ABORT("fatal error"); } +struct llama_device_memory_data { + int64_t total; + int64_t free; + llama_memory_breakdown_data mb; +}; + +static std::vector llama_get_device_memory_data( + const char * path_model, const llama_model_params * mparams, const llama_context_params * cparams, + std::vector & devs, uint32_t & hp_ngl, uint32_t & hp_n_ctx_train, uint32_t & hp_n_expert, + const ggml_log_level log_level) { + struct user_data_t { + struct { + ggml_log_callback callback; + void * user_data; + } original_logger; + ggml_log_level min_level; // prints below this log level go to debug log + }; + user_data_t ud; + llama_log_get(&ud.original_logger.callback, &ud.original_logger.user_data); + ud.min_level = log_level; + + llama_log_set([](ggml_log_level level, const char * text, void * user_data) { + const user_data_t * ud = (const user_data_t *) user_data; + const ggml_log_level level_eff = level >= ud->min_level ? level : GGML_LOG_LEVEL_DEBUG; + ud->original_logger.callback(level_eff, text, ud->original_logger.user_data); + }, &ud); + + llama_model_params mparams_copy = *mparams; + mparams_copy.no_alloc = true; + mparams_copy.use_mmap = false; + + llama_model * model = llama_model_load_from_file(path_model, mparams_copy); + if (model == nullptr) { + llama_log_set(ud.original_logger.callback, ud.original_logger.user_data); + throw std::runtime_error("failed to load model"); + } + + llama_context * ctx = llama_init_from_model(model, *cparams); + if (ctx == nullptr) { + llama_model_free(model); + llama_log_set(ud.original_logger.callback, ud.original_logger.user_data); + throw std::runtime_error("failed to create llama_context from model"); + } + + std::vector ret(model->devices.size()); + + std::map memory_breakdown = ctx->memory_breakdown(); + + for (const auto & [buft, mb] : memory_breakdown) { + if (ggml_backend_buft_is_host(buft)) { + continue; + } + + ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft); + if (!dev) { + continue; + } + for (size_t i = 0; i < ret.size(); i++) { + if (model->devices[i] == dev) { + ret[i].mb.model += mb.model; + ret[i].mb.context += mb.context; + ret[i].mb.compute += mb.compute; + break; + } + } + } + for (size_t i = 0; i < ret.size(); i++) { + size_t free, total; + ggml_backend_dev_memory(model->devices[i], &free, &total); + ret[i].free = free; + ret[i].total = total; + } + + devs = model->devices; + hp_ngl = model->hparams.n_layer; + hp_n_ctx_train = model->hparams.n_ctx_train; + hp_n_expert = model->hparams.n_expert; + + llama_memory_breakdown_print(ctx); // goes to debug log + + llama_free(ctx); + llama_model_free(model); + llama_log_set(ud.original_logger.callback, ud.original_logger.user_data); + return ret; +} + +// enum to identify part of a layer for distributing its tensors: +enum layer_fraction_t { + LAYER_FRACTION_NONE = 0, // nothing + LAYER_FRACTION_ATTN = 1, // attention + LAYER_FRACTION_UP = 2, // attention + up + LAYER_FRACTION_GATE = 3, // attention + up + gate + LAYER_FRACTION_MOE = 4, // everything but sparse MoE weights +}; +// this enum is only used in llama_params_fit_impl but needs to be defined outside of it to fix a Windows compilation issue + +static void llama_params_fit_impl( + const char * path_model, struct llama_model_params * mparams, struct llama_context_params * cparams, + float * tensor_split, struct llama_model_tensor_buft_override * tensor_buft_overrides, + size_t margin_s, uint32_t n_ctx_min, enum ggml_log_level log_level) { + constexpr int64_t MiB = 1024*1024; + const int64_t margin = margin_s; // this function uses int64_t rather than size_t for memory sizes to more conveniently handle deficits + typedef std::vector dmds_t; + const llama_model_params default_mparams = llama_model_default_params(); + + std::vector devs; + uint32_t hp_ngl = 0; // hparams.n_gpu_layers + uint32_t hp_nct = 0; // hparams.n_ctx_train + uint32_t hp_nex = 0; // hparams.n_expert + + // step 1: get data for default parameters and check whether any changes are necessary in the first place + + LLAMA_LOG_DEBUG("%s: getting device memory data for initial parameters:\n", __func__); + const dmds_t dmds_full = llama_get_device_memory_data(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level); + const size_t nd = devs.size(); // number of devices + if (nd == 0) { + LLAMA_LOG_INFO("%s: no devices with dedicated memory found\n", __func__); + return; + } + + std::vector dev_names; + { + dev_names.reserve(nd); + size_t max_length = 0; + for (ggml_backend_dev_t dev : devs) { + std::string name = ggml_backend_dev_name(dev); + name += " ("; + name += ggml_backend_dev_description(dev); + name += ")"; + dev_names.push_back(name); + max_length = std::max(max_length, name.length()); + } + for (std::string & dn : dev_names) { + dn.insert(dn.end(), max_length - dn.length(), ' '); + } + } + + int64_t sum_total = 0; + int64_t sum_projected_free = 0; + int64_t min_projected_free = INT64_MAX; + int64_t sum_projected_used = 0; + int64_t sum_projected_ctx = 0; + + if (nd > 1) { + LLAMA_LOG_INFO("%s: projected memory use with initial parameters [MiB]:\n", __func__); + } + for (size_t id = 0; id < nd; id++) { + const llama_device_memory_data & dmd = dmds_full[id]; + + const int64_t projected_used = dmd.mb.total(); + const int64_t projected_free = dmd.free - projected_used; + + sum_total += dmd.total; + sum_projected_used += projected_used; + sum_projected_free += projected_free; + min_projected_free = std::min(min_projected_free, projected_free); + sum_projected_ctx += dmd.mb.context; + + if (nd > 1) { + LLAMA_LOG_INFO("%s: - %s: %6" PRId64 " total, %6" PRId64 " used, %6" PRId64 " %s\n", + __func__, dev_names[id].c_str(), dmd.total/MiB, projected_used/MiB, std::abs(projected_free)/MiB, + projected_free >= 0 ? "surplus" : "deficit"); + } + } + assert(sum_total >= 0 && sum_projected_used >= 0 && sum_projected_ctx >= 0); + assert(sum_projected_used >= sum_projected_ctx); + LLAMA_LOG_INFO("%s: projected to use %" PRId64 " MiB of device memory vs. %" PRId64 " MiB of free device memory\n", + __func__, sum_projected_used/MiB, sum_total/MiB); + if (min_projected_free >= margin) { + if (nd == 1) { + LLAMA_LOG_INFO("%s: will leave %" PRId64 " >= %" PRId64 " MiB of free device memory, no changes needed\n", + __func__, min_projected_free/MiB, margin/MiB); + return; + } + LLAMA_LOG_INFO("%s: will leave at least %" PRId64 " >= %" PRId64 " MiB of free memory on all devices, no changes needed\n", + __func__, min_projected_free/MiB, margin/MiB); + return; + } + + // step 2: try reducing memory use by reducing the context size + + { + int64_t global_surplus = sum_projected_free - int64_t(nd)*margin; + if (global_surplus < 0) { + LLAMA_LOG_INFO(nd == 1 ? + "%s: cannot fulfill margin of %" PRId64 " MiB, need to reduce device memory by %" PRId64 " MiB\n" : + "%s: cannot fulfill margin of %" PRId64 " MiB on all devices, need to use %" PRId64 " MiB less in total\n", + __func__, margin/MiB, -global_surplus/MiB); + if (cparams->n_ctx == 0) { + if (hp_nct > n_ctx_min) { + const int64_t bytes_per_ctx = sum_projected_ctx / hp_nct; + const uint32_t ctx_reduction = std::min( + uint32_t((-global_surplus + bytes_per_ctx - 1) / bytes_per_ctx), hp_nct - n_ctx_min); + cparams->n_ctx = hp_nct - ctx_reduction; + const int64_t memory_reduction = ctx_reduction * bytes_per_ctx; + global_surplus += memory_reduction; + LLAMA_LOG_INFO("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n", + __func__, hp_nct, cparams->n_ctx, memory_reduction/MiB); + } else { + LLAMA_LOG_INFO("%s: default model context size is %" PRIu32 " which is <= the min. context size of %" PRIu32 " -> no change\n", + __func__, hp_nct, n_ctx_min); + } + } else { + LLAMA_LOG_INFO("%s: context size set by user to %" PRIu32 " -> no change\n", __func__, cparams->n_ctx); + } + } + if (global_surplus >= 0) { + LLAMA_LOG_INFO("%s: entire model can be fit across devices by reducing context\n", __func__); + return; + } + } + + if (mparams->n_gpu_layers != default_mparams.n_gpu_layers) { + throw std::runtime_error("n_gpu_layers already set by user to " + std::to_string(mparams->n_gpu_layers) + ", abort"); + } + if (nd > 1) { + if (!tensor_split) { + throw std::runtime_error("did not provide a buffer to write the tensor_split to, abort"); + } + if (mparams->tensor_split) { + for (size_t id = 0; id < nd; id++) { + if (mparams->tensor_split[id] != 0.0f) { + throw std::runtime_error("model_params::tensor_split already set by user, abort"); + } + } + } + if (mparams->split_mode == LLAMA_SPLIT_MODE_ROW) { + throw std::runtime_error("changing weight allocation for LLAMA_SPLIT_MODE_ROW not implemented, abort"); + } + if (hp_ngl < 2*nd) { + throw std::runtime_error("model has only " + std::to_string(hp_ngl) + " layers but need at least " + + std::to_string(2*nd) + " to fit memory for " + std::to_string(nd) + " devices, abort"); + } + } + if (!tensor_buft_overrides) { + throw std::runtime_error("did not provide buffer to set tensor_buft_overrides, abort"); + } + if (mparams->tensor_buft_overrides && (mparams->tensor_buft_overrides->pattern || mparams->tensor_buft_overrides->buft)) { + throw std::runtime_error("model_params::tensor_buft_overrides already set by user, abort"); + } + + // step 3: iteratively fill the back to front with "dense" layers + // - for a dense model simply fill full layers, giving each device a contiguous slice of the model + // - for a MoE model, same as dense model but with all MoE tensors in system memory + + // utility function that returns a static C string matching the tensors for a specific layer index and layer fraction: + auto get_overflow_pattern = [&](const size_t il, const layer_fraction_t lf) -> const char * { + constexpr size_t n_strings = 1000; + if (il >= n_strings) { + throw std::runtime_error("at most " + std::to_string(n_strings) + " model layers are supported"); + } + switch (lf) { + case LAYER_FRACTION_ATTN: { + static std::array patterns; + if (patterns[il].empty()) { + patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(up|gate|down).*"; + } + return patterns[il].c_str(); + } + case LAYER_FRACTION_UP: { + static std::array patterns; + if (patterns[il].empty()) { + patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(gate|down).*"; + } + return patterns[il].c_str(); + } + case LAYER_FRACTION_GATE: { + static std::array patterns; + if (patterns[il].empty()) { + patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_down.*"; + } + return patterns[il].c_str(); + } + case LAYER_FRACTION_MOE: { + static std::array patterns; + if (patterns[il].empty()) { + patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(up|down|gate)_(ch|)exps"; + } + return patterns[il].c_str(); + } + default: + GGML_ABORT("fatal error"); + } + }; + + struct ngl_t { + uint32_t n_layer = 0; // number of total layers + uint32_t n_part = 0; // number of partial layers, <= n_layer + + // for the first partial layer varying parts can overflow, all further layers use LAYER_FRACTION_MOE: + layer_fraction_t overflow_type = LAYER_FRACTION_MOE; + }; + + const size_t ntbo = llama_max_tensor_buft_overrides(); + + // utility function to set n_gpu_layers and tensor_split + auto set_ngl_tensor_split_tbo = [&]( + const std::vector & ngl_per_device, + const std::vector & overflow_bufts, + llama_model_params & mparams, + const bool add_nonrepeating) { + mparams.n_gpu_layers = 0; + for (size_t id = 0; id < nd; id++) { + mparams.n_gpu_layers += ngl_per_device[id].n_layer; + if (nd > 1) { + tensor_split[id] = ngl_per_device[id].n_layer; + } + } + assert(uint32_t(mparams.n_gpu_layers) <= hp_ngl); + uint32_t il0 = hp_ngl - mparams.n_gpu_layers; // start index for tensor buft overrides + + if (add_nonrepeating) { + mparams.n_gpu_layers += 1; + tensor_split[nd - 1] += 1; + } + mparams.tensor_split = tensor_split; + + size_t itbo = 0; + for (size_t id = 0; id < nd; id++) { + il0 += ngl_per_device[id].n_layer - ngl_per_device[id].n_part; + for (uint32_t il = il0; il < il0 + ngl_per_device[id].n_part; il++) { + if (itbo + 1 >= ntbo) { + tensor_buft_overrides[itbo].pattern = nullptr; + tensor_buft_overrides[itbo].buft = nullptr; + itbo++; + mparams.tensor_buft_overrides = tensor_buft_overrides; + throw std::runtime_error("llama_params_fit_n_tensor_buft_overrides() == " + + std::to_string(ntbo) + " is insufficient for model\n"); + } + tensor_buft_overrides[itbo].pattern = get_overflow_pattern(il, il == il0 ? ngl_per_device[id].overflow_type : LAYER_FRACTION_MOE); + tensor_buft_overrides[itbo].buft = overflow_bufts[id]; + itbo++; + } + il0 += ngl_per_device[id].n_part; + } + tensor_buft_overrides[itbo].pattern = nullptr; + tensor_buft_overrides[itbo].buft = nullptr; + itbo++; + mparams.tensor_buft_overrides = tensor_buft_overrides; + }; + + // utility function that returns the memory use per device for given numbers of layers per device + auto get_memory_for_layers = [&]( + const char * func_name, + const std::vector & ngl_per_device, + const std::vector & overflow_bufts, + const bool add_nonrepeating) -> std::vector { + llama_model_params mparams_copy = *mparams; + set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, mparams_copy, add_nonrepeating); + + const dmds_t dmd_nl = llama_get_device_memory_data( + path_model, &mparams_copy, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level); + + LLAMA_LOG_DEBUG("%s: memory for test allocation by device:\n", func_name); + for (size_t id = 0; id < nd; id++) { + const ngl_t & n = ngl_per_device[id]; + LLAMA_LOG_DEBUG( + "%s: id=%zu, n_layer=%2" PRIu32 ", n_part=%2" PRIu32 ", overflow_type=%d, mem=%6" PRId64 " MiB\n", + func_name, id, n.n_layer, n.n_part, int(n.overflow_type), dmd_nl[id].mb.total()/MiB); + } + + std::vector ret; + ret.reserve(nd); + for (const llama_device_memory_data & dmd : dmd_nl) { + ret.push_back(dmd.mb.total()); + } + return ret; + }; + + int64_t global_surplus_cpu_moe = 0; + if (hp_nex > 0) { + const static std::string pattern_moe_all = "blk\\.\\d+\\.ffn_(up|down|gate)_(ch|)exps"; // matches all MoE tensors + ggml_backend_buffer_type_t cpu_buft = ggml_backend_cpu_buffer_type(); + tensor_buft_overrides[0] = {pattern_moe_all.c_str(), cpu_buft}; + tensor_buft_overrides[1] = {nullptr, nullptr}; + mparams->tensor_buft_overrides = tensor_buft_overrides; + + LLAMA_LOG_DEBUG("%s: getting device memory data with all MoE tensors moved to system memory:\n", __func__); + const dmds_t dmds_cpu_moe = llama_get_device_memory_data( + path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level); + + for (const llama_device_memory_data & dmd : dmds_cpu_moe) { + global_surplus_cpu_moe += dmd.free; + global_surplus_cpu_moe -= int64_t(dmd.mb.total()) + margin; + } + + if (global_surplus_cpu_moe > 0) { + LLAMA_LOG_INFO("%s: with only dense weights in device memory there is a total surplus of %" PRId64 " MiB\n", + __func__, global_surplus_cpu_moe/MiB); + } else { + LLAMA_LOG_INFO("%s: with only dense weights in device memory there is still a total deficit of %" PRId64 " MiB\n", + __func__, -global_surplus_cpu_moe/MiB); + } + + // reset + tensor_buft_overrides[0] = {nullptr, nullptr}; + mparams->tensor_buft_overrides = tensor_buft_overrides; + } + + std::vector targets; // maximum acceptable memory use per device + targets.reserve(nd); + for (size_t id = 0; id < nd; id++) { + targets.push_back(dmds_full[id].free - margin); + LLAMA_LOG_DEBUG("%s: id=%zu, target=%" PRId64 " MiB\n", __func__, id, targets[id]/MiB); + } + + // whether for the optimal memory use we expect to load at least some MoE tensors: + const bool partial_moe = hp_nex > 0 && global_surplus_cpu_moe > 0; + + std::vector overflow_bufts; // which bufts the partial layers of a device overflow to: + overflow_bufts.reserve(nd); + for (size_t id = 0; id < nd - 1; ++id) { + overflow_bufts.push_back(ggml_backend_dev_buffer_type(devs[id + 1])); + } + overflow_bufts.push_back(ggml_backend_cpu_buffer_type()); + + std::vector ngl_per_device(nd); + std::vector mem = get_memory_for_layers(__func__, ngl_per_device, overflow_bufts, partial_moe); + if (hp_nex > 0) { + for (size_t id = 0; id < nd; id++) { + ngl_per_device[id].overflow_type = LAYER_FRACTION_MOE; + } + } + + // optimize the number of layers per device using the method of false position: + // - ngl_per_device has 0 layers for each device, lower bound + // - try a "high" configuration where a device is given all unassigned layers + // - interpolate the memory use / layer between low and high linearly to get a guess where it meets our target + // - check memory use of our guess, replace either the low or high bound + // - once we only have a difference of a single layer, stop and return the lower bound that just barely still fits + if (hp_nex == 0) { + LLAMA_LOG_INFO("%s: filling dense layers back-to-front:\n", __func__); + } else { + LLAMA_LOG_INFO("%s: filling dense-only layers back-to-front:\n", __func__); + } + uint32_t n_unassigned = hp_ngl; + for (int id = nd - 1; id >= 0; id--) { + std::vector ngl_per_device_high = ngl_per_device; + ngl_per_device_high[id].n_layer = n_unassigned; + if (hp_nex > 0) { + ngl_per_device_high[id].n_part = ngl_per_device_high[id].n_layer; + } + if (ngl_per_device_high[id].n_layer > 0) { + std::vector mem_high = get_memory_for_layers(__func__, ngl_per_device_high, overflow_bufts, partial_moe); + if (mem_high[id] > targets[id]) { + uint32_t delta = ngl_per_device_high[id].n_layer - ngl_per_device[id].n_layer; + while (delta > 1) { + uint32_t step_size = int64_t(delta) * (targets[id] - mem[id]) / (mem_high[id] - mem[id]); + step_size = std::max(step_size, uint32_t(1)); + step_size = std::min(step_size, delta - 1); + + std::vector ngl_per_device_test = ngl_per_device; + ngl_per_device_test[id].n_layer += step_size; + if (hp_nex) { + ngl_per_device_test[id].n_part += step_size; + } + const std::vector mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe); + + if (mem_test[id] <= targets[id]) { + ngl_per_device = ngl_per_device_test; + mem = mem_test; + n_unassigned -= ngl_per_device[id].n_layer; + LLAMA_LOG_DEBUG("%s: set ngl_per_device[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer); + } else { + ngl_per_device_high = ngl_per_device_test; + mem_high = mem_test; + LLAMA_LOG_DEBUG("%s: set ngl_per_device_high[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer); + } + delta = ngl_per_device_high[id].n_layer - ngl_per_device[id].n_layer; + } + } else { + ngl_per_device = ngl_per_device_high; + n_unassigned -= ngl_per_device[id].n_layer; + LLAMA_LOG_DEBUG("%s: set ngl_per_device[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer); + } + } + + const int64_t projected_margin = dmds_full[id].free - mem[id]; + LLAMA_LOG_INFO( + "%s: - %s: %2" PRIu32 " layers, %6" PRId64 " MiB used, %6" PRId64 " MiB free\n", + __func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, mem[id]/MiB, projected_margin/MiB); + } + if (hp_nex == 0 || global_surplus_cpu_moe <= 0) { + set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, *mparams, partial_moe); + return; + } + + // step 4: for a MoE model where all dense tensors fit, + // convert the dense-only layers in the back to full layers in the front until all devices are full + // essentially the same procedure as for the dense-only layers except front-to-back + // also, try fitting at least part of one more layer to reduce waste for "small" GPUs with e.g. 24 GiB VRAM + + size_t id_dense_start = nd; + for (int id = nd - 1; id >= 0; id--) { + if (ngl_per_device[id].n_layer > 0) { + id_dense_start = id; + continue; + } + break; + } + assert(id_dense_start < nd); + + LLAMA_LOG_INFO("%s: converting dense-only layers to full layers and filling them front-to-back with overflow to next device/system memory:\n", __func__); + for (size_t id = 0; id <= id_dense_start; id++) { + std::vector ngl_per_device_high = ngl_per_device; + for (size_t jd = id_dense_start; jd < nd; jd++) { + const uint32_t n_layer_move = ngl_per_device_high[jd].n_layer; + ngl_per_device_high[id].n_layer += n_layer_move; + ngl_per_device_high[jd].n_layer -= n_layer_move; + ngl_per_device_high[jd].n_part = 0; + } + size_t id_dense_start_high = nd - 1; + std::vector mem_high = get_memory_for_layers(__func__, ngl_per_device_high, overflow_bufts, partial_moe); + + if (mem_high[id] > targets[id]) { + assert(ngl_per_device_high[id].n_layer >= ngl_per_device_high[id].n_part); + assert(ngl_per_device[id].n_layer >= ngl_per_device[id].n_part); + assert((ngl_per_device_high[id].n_layer - ngl_per_device_high[id].n_part) + >= ngl_per_device[id].n_layer - ngl_per_device[id].n_part); + uint32_t delta = (ngl_per_device_high[id].n_layer - ngl_per_device_high[id].n_part) + - (ngl_per_device[id].n_layer - ngl_per_device[id].n_part); + while (delta > 1) { + uint32_t step_size = int64_t(delta) * (targets[id] - mem[id]) / (mem_high[id] - mem[id]); + step_size = std::max(step_size, uint32_t(1)); + step_size = std::min(step_size, delta - 1); + + std::vector ngl_per_device_test = ngl_per_device; + size_t id_dense_start_test = id_dense_start; + uint32_t n_converted_test = 0; + for (;id_dense_start_test < nd; id_dense_start_test++) { + const uint32_t n_convert_jd = std::min(step_size - n_converted_test, ngl_per_device_test[id_dense_start_test].n_part); + ngl_per_device_test[id_dense_start_test].n_layer -= n_convert_jd; + ngl_per_device_test[id_dense_start_test].n_part -= n_convert_jd; + ngl_per_device_test[id].n_layer += n_convert_jd; + n_converted_test += n_convert_jd; + + if (ngl_per_device_test[id_dense_start_test].n_layer > 0) { + break; + } + } + const std::vector mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe); + + if (mem_test[id] <= targets[id]) { + ngl_per_device = ngl_per_device_test; + mem = mem_test; + id_dense_start = id_dense_start_test; + LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start=%zu\n", + __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start); + } else { + ngl_per_device_high = ngl_per_device_test; + mem_high = mem_test; + id_dense_start_high = id_dense_start_test; + LLAMA_LOG_DEBUG("%s: set ngl_per_device_high[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start_high=%zu\n", + __func__, id, ngl_per_device_high[id].n_layer, ngl_per_device_high[id].n_part, id_dense_start_high); + } + delta = (ngl_per_device_high[id].n_layer - ngl_per_device_high[id].n_part) + - (ngl_per_device[id].n_layer - ngl_per_device[id].n_part); + } + } else { + ngl_per_device = ngl_per_device_high; + id_dense_start = id_dense_start_high; + LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start=%zu\n", + __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start); + } + + // try to fit at least part of one more layer + if (ngl_per_device[id_dense_start].n_layer > 0) { + std::vector ngl_per_device_test = ngl_per_device; + size_t id_dense_start_test = id_dense_start; + ngl_per_device_test[id_dense_start_test].n_layer--; + ngl_per_device_test[id_dense_start_test].n_part--; + ngl_per_device_test[id].n_layer++; + ngl_per_device_test[id].n_part++; + if (ngl_per_device_test[id_dense_start_test].n_layer == 0) { + id_dense_start_test++; + } + ngl_per_device_test[id].overflow_type = LAYER_FRACTION_UP; + LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_UP\n", __func__); + std::vector mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe); + if (mem_test[id] < targets[id]) { + ngl_per_device = ngl_per_device_test; + mem = mem_test; + id_dense_start = id_dense_start_test; + LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", UP), id_dense_start=%zu\n", + __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start); + + ngl_per_device_test[id].overflow_type = LAYER_FRACTION_GATE; + LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_GATE\n", __func__); + mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe); + if (mem_test[id] < targets[id]) { + ngl_per_device = ngl_per_device_test; + mem = mem_test; + id_dense_start = id_dense_start_test; + LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", GATE), id_dense_start=%zu\n", + __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start); + } + } else { + ngl_per_device_test[id].overflow_type = LAYER_FRACTION_ATTN; + LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_ATTN\n", __func__); + mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe); + if (mem_test[id] < targets[id]) { + ngl_per_device = ngl_per_device_test; + mem = mem_test; + id_dense_start = id_dense_start_test; + LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", ATTN), id_dense_start=%zu\n", + __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start); + } + } + } + + const int64_t projected_margin = dmds_full[id].free - mem[id]; + LLAMA_LOG_INFO( + "%s: - %s: %2" PRIu32 " layers (%2" PRIu32 " overflowing), %6" PRId64 " MiB used, %6" PRId64 " MiB free\n", + __func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, ngl_per_device[id].n_part, mem[id]/MiB, projected_margin/MiB); + } + + set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, *mparams, partial_moe); +} + +bool llama_params_fit( + const char * path_model, struct llama_model_params * mparams, struct llama_context_params * cparams, + float * tensor_split, struct llama_model_tensor_buft_override * tensor_buft_overrides, + size_t margin_s, uint32_t n_ctx_min, enum ggml_log_level log_level) { + const int64_t t0_us = llama_time_us(); + bool ok = true; + try { + llama_params_fit_impl(path_model, mparams, cparams, tensor_split, tensor_buft_overrides, margin_s, n_ctx_min, log_level); + LLAMA_LOG_INFO("%s: successfully fit params to free device memory\n", __func__); + } catch (const std::runtime_error & e) { + LLAMA_LOG_WARN("%s: failed to fit params to free device memory: %s\n", __func__, e.what()); + ok = false; + } + const int64_t t1_us = llama_time_us(); + LLAMA_LOG_INFO("%s: fitting params to free memory took %.2f seconds\n", __func__, (t1_us - t0_us) * 1e-6); + return ok; +} + struct llama_sampler_chain_params llama_sampler_chain_default_params() { struct llama_sampler_chain_params result = { /*.no_perf =*/ true, @@ -49,6 +692,10 @@ size_t llama_max_devices(void) { return 16; } +size_t llama_max_tensor_buft_overrides() { + return 4096; +} + bool llama_supports_mmap(void) { return llama_mmap::SUPPORTED; } @@ -108,11 +755,12 @@ static int llama_model_load(const std::string & fname, std::vector model.t_start_us = tm.t_start_us; try { - llama_model_loader ml(fname, splits, params.use_mmap, params.check_tensors, params.kv_overrides, params.tensor_buft_overrides); + llama_model_loader ml(fname, splits, params.use_mmap, params.check_tensors, params.no_alloc, params.kv_overrides, params.tensor_buft_overrides); ml.print_info(); model.hparams.vocab_only = params.vocab_only; + model.hparams.no_alloc = params.no_alloc; try { model.load_arch(ml); diff --git a/src/models/deepseek2.cpp b/src/models/deepseek2.cpp index dbaa8297be..49382874ba 100644 --- a/src/models/deepseek2.cpp +++ b/src/models/deepseek2.cpp @@ -1,7 +1,5 @@ #include "models.h" - - llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B @@ -20,9 +18,15 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly. // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation. - const float mscale = attn_factor * (1.0f + hparams.rope_yarn_log_mul * logf(1.0f / freq_scale)); - const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k)); - const float attn_factor = 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale)); + // And also: https://github.com/ggml-org/llama.cpp/pull/17945 [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX] + + // first cancel the adjustment from llama_hparams::yarn_attn_factor_adjust to get the original attn_factor + GGML_ASSERT(ext_factor >= 0.0f); + const float attn_factor_org = attn_factor * (1.0f + 0.1f * logf(1.0f / freq_scale)); + + // use the original attn_factor to pre-scale the kq_scale + const float mscale = attn_factor_org * (1.0f + 0.1f * hparams.rope_yarn_log_mul * logf(1.0f / freq_scale)); + const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k)); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/gemma3-iswa.cpp b/src/models/gemma3.cpp similarity index 78% rename from src/models/gemma3-iswa.cpp rename to src/models/gemma3.cpp index 839ff6d3d9..ae60ef4790 100644 --- a/src/models/gemma3-iswa.cpp +++ b/src/models/gemma3.cpp @@ -1,6 +1,7 @@ #include "models.h" -llm_build_gemma3_iswa::llm_build_gemma3_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { +template +llm_build_gemma3::llm_build_gemma3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_k; ggml_tensor * cur; @@ -17,13 +18,28 @@ llm_build_gemma3_iswa::llm_build_gemma3_iswa(const llama_model & model, const ll ggml_tensor * inp_pos = build_inp_pos(); // TODO: is causal == true correct? might need some changes - auto * inp_attn = build_attn_inp_kv_iswa(); + using inp_attn_type = std::conditional_t; + inp_attn_type * inp_attn = nullptr; + + if constexpr (iswa) { + inp_attn = build_attn_inp_kv_iswa(); + } else { + inp_attn = build_attn_inp_kv(); + } ggml_tensor * inp_out_ids = build_inp_out_ids(); for (int il = 0; il < n_layer; ++il) { - const float freq_base_l = model.get_rope_freq_base (cparams, il); - const float freq_scale_l = model.get_rope_freq_scale(cparams, il); + float freq_base_l = 0.0f; + float freq_scale_l = 0.0f; + + if constexpr (iswa) { + freq_base_l = model.get_rope_freq_base (cparams, il); + freq_scale_l = model.get_rope_freq_scale(cparams, il); + } else { + freq_base_l = freq_base; + freq_scale_l = freq_scale; + } // norm cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); @@ -102,7 +118,7 @@ llm_build_gemma3_iswa::llm_build_gemma3_iswa(const llama_model & model, const ll cur = build_norm(cur, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, -1); - cb(cur, "ffn_post_norm", -1); + cb(cur, "ffn_post_norm", il); cur = ggml_add(ctx0, cur, sa_out); @@ -124,8 +140,17 @@ llm_build_gemma3_iswa::llm_build_gemma3_iswa(const llama_model & model, const ll // lm_head cur = build_lora_mm(model.output, cur); + if (hparams.f_final_logit_softcapping) { + cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping); + cur = ggml_tanh(ctx0, cur); + cur = ggml_scale(ctx0, cur, hparams.f_final_logit_softcapping); + } + cb(cur, "result_output", -1); res->t_logits = cur; ggml_build_forward_expand(gf, cur); } + +template struct llm_build_gemma3; +template struct llm_build_gemma3; diff --git a/src/models/models.h b/src/models/models.h index d93601ad06..6494f54501 100644 --- a/src/models/models.h +++ b/src/models/models.h @@ -179,8 +179,9 @@ struct llm_build_gemma2_iswa : public llm_graph_context { llm_build_gemma2_iswa(const llama_model & model, const llm_graph_params & params); }; -struct llm_build_gemma3_iswa : public llm_graph_context { - llm_build_gemma3_iswa(const llama_model & model, const llm_graph_params & params); +template +struct llm_build_gemma3 : public llm_graph_context { + llm_build_gemma3(const llama_model & model, const llm_graph_params & params); }; struct llm_build_gemma3n_iswa : public llm_graph_context { diff --git a/src/models/qwen2.cpp b/src/models/qwen2.cpp index 587a932426..3da4dea3c1 100644 --- a/src/models/qwen2.cpp +++ b/src/models/qwen2.cpp @@ -31,16 +31,25 @@ llm_build_qwen2::llm_build_qwen2(const llama_model & model, const llm_graph_para { // compute Q and K and RoPE them ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); - Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); cb(Qcur, "Qcur", il); + if (model.layers[il].bq) { + Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + cb(Qcur, "Qcur", il); + } ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur); - Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); cb(Kcur, "Kcur", il); + if (model.layers[il].bk) { + Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + cb(Kcur, "Kcur", il); + } ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur); - Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); cb(Vcur, "Vcur", il); + if (model.layers[il].bv) { + Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + cb(Vcur, "Vcur", il); + } Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 9ba559c8df..c3d9f9c324 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -222,6 +222,14 @@ llama_build_and_test(test-backend-ops.cpp) llama_build_and_test(test-model-load-cancel.cpp LABEL "model") llama_build_and_test(test-autorelease.cpp LABEL "model") +# Test for state restore with fragmented KV cache +# Requires a model, uses same args pattern as test-thread-safety +if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "s390x") + llama_build_and_test(test-state-restore-fragmented.cpp LABEL "model" ARGS -hf ggml-org/models -hff tinyllamas/stories15M-q4_0.gguf) +else() + llama_build_and_test(test-state-restore-fragmented.cpp LABEL "model" ARGS -hf ggml-org/models -hff tinyllamas/stories15M-be.Q4_0.gguf) +endif() + if (NOT GGML_BACKEND_DL) # these tests use the backends directly and cannot be built with dynamic loading llama_build_and_test(test-barrier.cpp) diff --git a/tests/test-arg-parser.cpp b/tests/test-arg-parser.cpp index a60ca12fe5..468d325e22 100644 --- a/tests/test-arg-parser.cpp +++ b/tests/test-arg-parser.cpp @@ -20,20 +20,20 @@ int main(void) { std::unordered_set seen_env_vars; for (const auto & opt : ctx_arg.options) { // check for args duplications - for (const auto & arg : opt.args) { + for (const auto & arg : opt.get_args()) { if (seen_args.find(arg) == seen_args.end()) { seen_args.insert(arg); } else { - fprintf(stderr, "test-arg-parser: found different handlers for the same argument: %s", arg); + fprintf(stderr, "test-arg-parser: found different handlers for the same argument: %s", arg.c_str()); exit(1); } } // check for env var duplications - if (opt.env) { - if (seen_env_vars.find(opt.env) == seen_env_vars.end()) { - seen_env_vars.insert(opt.env); + for (const auto & env : opt.get_env()) { + if (seen_env_vars.find(env) == seen_env_vars.end()) { + seen_env_vars.insert(env); } else { - fprintf(stderr, "test-arg-parser: found different handlers for the same env var: %s", opt.env); + fprintf(stderr, "test-arg-parser: found different handlers for the same env var: %s", env.c_str()); exit(1); } } @@ -72,6 +72,10 @@ int main(void) { argv = {"binary_name", "--draft", "123"}; assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_EMBEDDING)); + // negated arg + argv = {"binary_name", "--no-mmap"}; + assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + printf("test-arg-parser: test valid usage\n\n"); @@ -115,6 +119,14 @@ int main(void) { assert(params.model.path == "blah.gguf"); assert(params.cpuparams.n_threads == 1010); + printf("test-arg-parser: test negated environment variables\n\n"); + + setenv("LLAMA_ARG_MMAP", "0", true); + setenv("LLAMA_ARG_NO_PERF", "1", true); // legacy format + argv = {"binary_name"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.use_mmap == false); + assert(params.no_perf == true); printf("test-arg-parser: test environment variables being overwritten\n\n"); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 2e94a53da2..416218b5b8 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -5875,7 +5875,7 @@ struct test_flash_attn_ext : public test_case { ggml_tensor * m = nullptr; if (mask) { - m = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, GGML_PAD(nb, GGML_KQ_MASK_PAD), 1, nr23[1]); + m = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, nb, 1, nr23[1]); ggml_set_name(m, "m"); } @@ -6253,6 +6253,31 @@ struct test_solve_tri : public test_case { } }; +// GGML_OP_DIAG +struct test_diag : public test_case { + const ggml_type type; + const std::array ne; + + std::string vars() override { return VARS_TO_STR2(type, ne); } + + test_diag(ggml_type type = GGML_TYPE_F32, + std::array ne = { 10, 1, 4, 3 }) + : type(type), ne(ne) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + GGML_ASSERT(ne[1] == 1); + ggml_tensor * a = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]); + ggml_set_param(a); + ggml_set_name(a, "a"); + + ggml_tensor * out = ggml_diag(ctx, a); + ggml_set_name(out, "out"); + + return out; + } +}; + + enum llm_norm_type { LLM_NORM, LLM_NORM_RMS, @@ -7627,6 +7652,9 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true, true, GGML_TYPE_F32, {1, 1}, 0.1f, 8.0f)); test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true, true, GGML_TYPE_F16, {1, 1}, 0.1f, 8.0f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {200001, 2, 3, 1}, true, true, GGML_TYPE_F32, {1, 1}, 0.1f, 8.0f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {200001, 2, 3, 1}, true, true, GGML_TYPE_F16, {1, 1}, 0.1f, 8.0f)); + for (float max_bias : {0.0f, 8.0f}) { for (float scale : {1.0f, 0.1f}) { for (int64_t ne0 : {16, 1024}) { @@ -7826,15 +7854,34 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_fill(-152.0f, GGML_TYPE_F32, { 800, 600, 4, 4 })); test_cases.emplace_back(new test_fill(3.5f, GGML_TYPE_F32, { 2048, 512, 2, 2 })); + test_cases.emplace_back(new test_diag()); + test_cases.emplace_back(new test_diag(GGML_TYPE_F32, { 79, 1, 19, 13 })); + test_cases.emplace_back(new test_diag(GGML_TYPE_F32, { 256, 1, 8, 16 })); + test_cases.emplace_back(new test_solve_tri()); test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 11, 11, 1, 1 }, { 5, 11, 1, 1 })); test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 17, 17, 2, 4 }, { 9, 17, 2, 4 })); test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 30, 30, 7, 1 }, { 8, 30, 7, 1 })); test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 42, 42, 5, 2 }, { 10, 42, 5, 2 })); test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 2, 2 }, { 10, 64, 2, 2 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 2, 2 }, { 64, 64, 2, 2 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 79, 79, 5, 3 }, { 417, 79, 5, 3 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 2 }, { 32, 128, 4, 2 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 80, 80, 2, 8 }, { 80, 80, 2, 8 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 80, 80, 2, 8 }, { 79, 80, 2, 8 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 80, 80, 2, 8 }, { 81, 80, 2, 8 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 80, 80, 8, 8 }, { 80, 80, 8, 8 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 80, 80, 8, 8 }, { 79, 80, 8, 8 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 80, 80, 8, 8 }, { 81, 80, 8, 8 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 84, 84, 4, 4 }, { 32, 84, 4, 4 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 95, 95, 8, 8 }, { 40, 95, 8, 8 })); test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 100, 100, 4, 4 }, { 41, 100, 4, 4 })); test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 4 }, { 31, 128, 4, 4 })); - test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 300, 64, 4, 4 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 4 }, { 32, 128, 4, 4 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 3, 4 }, { 32, 128, 3, 4 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 1 }, { 32, 128, 4, 1 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 200, 64, 4, 4 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 384, 64, 4, 4 })); for (bool v : {false, true}) { for (bool circular : {false, true}) { @@ -7927,8 +7974,12 @@ static std::vector> make_test_cases_eval() { for (bool with_norm : {false, true}) { test_cases.emplace_back(new test_topk_moe({8, 22, 1, 1}, 4, with_norm)); + test_cases.emplace_back(new test_topk_moe({31, 22, 1, 1}, 8, with_norm)); test_cases.emplace_back(new test_topk_moe({32, 22, 1, 1}, 8, with_norm)); + test_cases.emplace_back(new test_topk_moe({40, 22, 1, 1}, 8, with_norm)); + test_cases.emplace_back(new test_topk_moe({71, 22, 1, 1}, 8, with_norm)); test_cases.emplace_back(new test_topk_moe({128, 1, 1, 1}, 128, with_norm)); + test_cases.emplace_back(new test_topk_moe({129, 1, 1, 1}, 128, with_norm)); } test_cases.emplace_back(new test_topk_moe({ 8, 22, 1, 1 }, 4, /*with_norm*/ false, /*delayed_softmax*/ true)); @@ -8035,12 +8086,13 @@ static std::vector> make_test_cases_perf() { test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 16416, 1, 128, {8, 1}, {4, 1}, {0, 2, 1, 3})); test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 1, 16416, {8, 1}, {4, 1}, {0, 1, 2, 3}, 2*16416)); - test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 2 }, { 6, 64, 4, 2 })); - test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 1 }, { 8, 128, 4, 1 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 32, 64, 4, 4 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 2 }, { 32, 128, 4, 2 })); // qwen3next with CHUNK_SIZE 64 test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 8, 32 }, { 64, 64, 8, 32 })); // qwen3next with CHUNK_SIZE 128 test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 32 }, { 128, 128, 4, 32 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 256, 256, 4, 2 }, { 128, 256, 4, 2 })); test_cases.emplace_back(new test_tri(GGML_TRI_TYPE_LOWER, GGML_TYPE_F32, { 256, 256, 4, 4 })); test_cases.emplace_back(new test_tri(GGML_TRI_TYPE_UPPER_DIAG, GGML_TYPE_F32, { 1024, 1024, 8, 4 })); @@ -8164,6 +8216,13 @@ static std::vector> make_test_cases_perf() { } } + // Examples from granite-4.0-h-1b/ggml-model-Q8_0.gguf + test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {515, 3328, 1, 1}, {4, 3328, 1, 1})); // prefill + test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {4, 3328, 1, 1}, {4, 3328, 1, 1})); // generate + test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 128, 64, 48, 1, 512, 1)); // prefill + test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 128, 64, 48, 1, 1, 1)); // generate + + return test_cases; } diff --git a/tests/test-barrier.cpp b/tests/test-barrier.cpp index 04c27761dc..61f73adfd2 100644 --- a/tests/test-barrier.cpp +++ b/tests/test-barrier.cpp @@ -11,19 +11,7 @@ #define MAX_NARGS 2 -int main(int argc, char *argv[]) { - - int n_threads = std::max(1, std::min(4, (int) std::thread::hardware_concurrency())); - int n_rounds = 100; - - if (argc > 1) { - n_threads = std::atoi(argv[1]); - } - - if (argc > 2) { - n_rounds = std::atoi(argv[2]); - } - +static void test_barrier(int n_threads, int n_rounds) { struct ggml_init_params params = { /* .mem_size = */ 1024*1024*1024, /* .mem_buffer = */ NULL, @@ -56,7 +44,7 @@ int main(int argc, char *argv[]) { exit(1); } - // Create compute plan + // The test runs with constant number of threads struct ggml_cplan cplan = ggml_graph_plan(gf, n_threads, threadpool); std::vector work_data(cplan.work_size); @@ -89,6 +77,160 @@ int main(int argc, char *argv[]) { ggml_threadpool_free(threadpool); ggml_free(ctx); +} + +static void test_active(int n_threads, int n_rounds) { + struct ggml_init_params params = { + /* .mem_size = */ 1024*1024*1024, + /* .mem_buffer = */ NULL, + /* .no_alloc = */ false, + }; + + struct ggml_context * ctx = ggml_init(params); + + // Create graph + struct ggml_cgraph * gf = ggml_new_graph(ctx); + + // Small graph with, parallel ops with barriers + struct ggml_tensor * out = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 64); + for (int i = 0; i < 2; i++) { + struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 64, 128); + out = ggml_mul_mat(ctx, a, out); + + struct ggml_tensor * d = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 128, 64); + out = ggml_mul_mat(ctx, d, out); + } + + ggml_build_forward_expand(gf, out); + int n_nodes = ggml_graph_n_nodes(gf); + + // Create threadpool + struct ggml_threadpool_params tpp = ggml_threadpool_params_default(n_threads); + struct ggml_threadpool* threadpool = ggml_threadpool_new(&tpp); + if (!threadpool) { + fprintf(stderr, "threadpool create failed : n_threads %d\n", n_threads); + exit(1); + } + + std::cerr << "graph-compute with" + << "\n n_threads: " << n_threads + << "\n n_nodes: " << n_nodes + << "\n n_rounds: " << n_rounds + << "\n"; + // ggml_graph_print(gf); + + // In this test we keep changing the number of threads every 4th iteration + // to test for race conditions in that path + + for (int i=0; i < n_rounds; i++) { + struct ggml_cplan cplan = ggml_graph_plan(gf, (i % 4) == 0 ? 1 : n_threads, threadpool); + + std::vector work_data(cplan.work_size); + cplan.work_data = work_data.data(); + + ggml_graph_compute(gf, &cplan); + } + + ggml_threadpool_free(threadpool); + ggml_free(ctx); +} + +static void test_multi_graph(int n_threads, int n_rounds) { + struct ggml_init_params params = { + /* .mem_size = */ 1024*1024*1024, + /* .mem_buffer = */ NULL, + /* .no_alloc = */ false, + }; + + struct ggml_context * ctx = ggml_init(params); + + // Create graphs + struct ggml_cgraph * gf0 = ggml_new_graph(ctx); + { + // Small graph with parallel ops with barriers + struct ggml_tensor * out = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 64); + for (int i = 0; i < 2; i++) { + struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 64, 128); + out = ggml_mul_mat(ctx, a, out); + + struct ggml_tensor * d = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 128, 64); + out = ggml_mul_mat(ctx, d, out); + } + + ggml_build_forward_expand(gf0, out); + } + + struct ggml_cgraph * gf1 = ggml_new_graph(ctx); + { + // Small graph with parallel ops with barriers + // Use larger tensors to make sure work_data size is larger than gf0 + struct ggml_tensor * out = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 256); + for (int i = 0; i < 4; i++) { + struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 256, 128); + out = ggml_mul_mat(ctx, a, out); + + struct ggml_tensor * d = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 128, 256); + out = ggml_mul_mat(ctx, d, out); + } + + ggml_build_forward_expand(gf1, out); + } + + + // Create threadpool + struct ggml_threadpool_params tpp = ggml_threadpool_params_default(n_threads); + struct ggml_threadpool* threadpool = ggml_threadpool_new(&tpp); + if (!threadpool) { + fprintf(stderr, "threadpool create failed : n_threads %d\n", n_threads); + exit(1); + } + + std::cerr << "graph-compute with" + << "\n gf0 n_nodes: " << ggml_graph_n_nodes(gf0) + << "\n gf1 n_nodes: " << ggml_graph_n_nodes(gf1) + << "\n n_threads: " << n_threads + << "\n n_rounds: " << n_rounds + << "\n"; + + // In this test we keep changing the number of threads every 4th iteration + // and we compute two graphs back to back to test graph frequent graph switching + + for (int i=0; i < n_rounds; i++) { + struct ggml_cplan cplan0 = ggml_graph_plan(gf0, (i % 4) == 0 ? 1 : n_threads, threadpool); + std::vector work_data0(cplan0.work_size); + cplan0.work_data = work_data0.data(); + + struct ggml_cplan cplan1 = ggml_graph_plan(gf1, (i % 4) == 0 ? 1 : n_threads, threadpool); + std::vector work_data1(cplan1.work_size); + cplan1.work_data = work_data1.data(); + + ggml_graph_compute(gf0, &cplan0); + ggml_graph_compute(gf1, &cplan1); + } + + ggml_threadpool_free(threadpool); + ggml_free(ctx); +} + + +int main(int argc, char *argv[]) { + + int n_threads = std::max(1, std::min(4, (int) std::thread::hardware_concurrency())); + int n_rounds = 100; + + if (argc > 1) { + n_threads = std::atoi(argv[1]); + } + + if (argc > 2) { + n_rounds = std::atoi(argv[2]); + } + + test_barrier(n_threads, n_rounds); + + test_active(n_threads, n_rounds * 100); + + test_multi_graph(n_threads, n_rounds * 10); return 0; } diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp index 45ee7f8388..8ea348642e 100644 --- a/tests/test-chat.cpp +++ b/tests/test-chat.cpp @@ -539,6 +539,71 @@ const common_chat_msg message_assist_call_python_lines = simple_assist const common_chat_msg message_assist_call_python_lines_unclosed = simple_assist_msg("", "", "python", "{\"code\":\"# This is a program:\\nprint('hey')"); const common_chat_msg message_assist_call_code_interpreter = simple_assist_msg("", "", "code_interpreter", "{\"code\":\"print('hey')\"}"); +// Use for PEG parser implementations +struct peg_test_case { + common_chat_templates_inputs params; + std::string input; + common_chat_msg expect; +}; + +struct make_peg_parser { + common_chat_params params_; + common_peg_arena arena_; + + make_peg_parser(common_chat_templates * tmpls, const common_chat_templates_inputs & inputs) { + params_ = common_chat_templates_apply(tmpls, inputs); + arena_.load(params_.parser); + } + + common_chat_msg parse(const std::string & msg, bool is_partial) { + return common_chat_peg_parse(arena_, msg, is_partial, /* syntax = */ {params_.format}); + } +}; + +static void test_peg_parser(common_chat_templates * tmpls, const std::function & init) { + peg_test_case tc; + init(tc); + if (tc.params.messages.empty()) { + tc.params.messages = {message_user}; + } + if (tc.expect.role.empty()) { + tc.expect.role = "assistant"; + } + + auto parser = make_peg_parser(tmpls, tc.params); + + common_chat_msg msg_accum; + common_chat_msg msg_prev; + msg_accum.role = msg_prev.role = "assistant"; + + for (size_t i = 1; i <= tc.input.size(); ++i) { + auto is_partial = i < tc.input.size(); + common_chat_msg msg_current = parser.parse(tc.input.substr(0, i), is_partial); + + for (const auto & diff : common_chat_msg_diff::compute_diffs(msg_prev, msg_current)) { + if (!diff.reasoning_content_delta.empty()) { + msg_accum.reasoning_content += diff.reasoning_content_delta; + } + if (!diff.content_delta.empty()) { + msg_accum.content += diff.content_delta; + } + if (diff.tool_call_index != std::string::npos) { + if (!diff.tool_call_delta.name.empty()) { + msg_accum.tool_calls.push_back({diff.tool_call_delta.name, "", ""}); + } + if (!diff.tool_call_delta.arguments.empty()) { + msg_accum.tool_calls.back().arguments += diff.tool_call_delta.arguments; + } + } + } + assert_msg_equals(msg_current, msg_accum, true); + msg_prev = msg_current; + } + + assert_msg_equals(tc.expect, parser.parse(tc.input, false), true); + assert_msg_equals(tc.expect, msg_accum, true); +} + static void test_msgs_oaicompat_json_conversion() { printf("[%s]\n", __func__); std::vector msgs{ @@ -3595,7 +3660,95 @@ Hey there!<|im_end|> /* ignore_whitespace_differences= */ true ); } +} +static void test_template_output_peg_parsers() { + printf("[%s]\n", __func__); + + // JSON schemas + const char * invoice_schema = R"({ + "type": "object", + "properties": { + "amount": {"type": "number"}, + "date": {"type": "string"} + } + })"; + + { + // Ministral-3-14B-Reasoning-2512 + auto tmpls = read_templates("models/templates/mistralai-Ministral-3-14B-Reasoning-2512.jinja"); + + // Test basic message + test_peg_parser(tmpls.get(), [&](auto & t) { + t.input = "Hello, world!\nWhat's up?"; + t.expect = message_assist; + }); + + // Test basic message and reasoning with reasoning_format = none + test_peg_parser(tmpls.get(), [&](auto & t) { + t.input = "[THINK]I'm\nthinking[/THINK]Hello, world!\nWhat's up?"; + t.expect.content = "[THINK]I'm\nthinking[/THINK]Hello, world!\nWhat's up?"; + }); + + // Test basic message and reasoning with reasoning_format = auto + test_peg_parser(tmpls.get(), [&](auto & t) { + t.input = "[THINK]I'm\nthinking[/THINK]Hello, world!\nWhat's up?"; + t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; + + t.expect = message_assist_thoughts; + }); + + // Test tool call + test_peg_parser(tmpls.get(), [&](auto & t) { + t.input = R"([TOOL_CALLS]special_function[ARGS]{"arg1":1})"; + t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; + t.params.tools = {special_function_tool}; + + t.expect = message_assist_call; + }); + + // Test tool call with reasoning + test_peg_parser(tmpls.get(), [&](auto & t) { + t.input = "[THINK]I'm\nthinking[/THINK]" + R"([TOOL_CALLS]special_function[ARGS]{"arg1":1})"; + t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; + t.params.tools = {special_function_tool}; + + t.expect = message_assist_call_thoughts; + }); + + // Test parallel tool calls + test_peg_parser(tmpls.get(), [&](auto & t) { + t.input = R"([TOOL_CALLS]special_function[ARGS]{"arg1": 1})" + R"([TOOL_CALLS]special_function_with_opt[ARGS]{"arg1": 1, "arg2": 2})"; + t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; + t.params.parallel_tool_calls = true; + t.params.tools = {special_function_tool, special_function_tool_with_optional_param}; + + t.expect.tool_calls = {{ + /* .name = */ "special_function", + /* .arguments = */ R"({"arg1": 1})", + /* .id = */ {}, + }, { + /* .name = */ "special_function_with_opt", + /* .arguments = */ R"({"arg1": 1, "arg2": 2})", + /* .id = */ {}, + }}; + }); + + // Test response format + test_peg_parser(tmpls.get(), [&](auto & t) { + t.input = "[THINK]I need to output the invoice details in JSON[/THINK]" + "```json\n" + R"({"amount": 123.45, "date": "2025-12-03"})" + "\n```"; + t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; + t.params.json_schema = invoice_schema; + + t.expect.reasoning_content = "I need to output the invoice details in JSON"; + t.expect.content =R"({"amount": 123.45, "date": "2025-12-03"})"; + }); + } } static void test_msg_diffs_compute() { @@ -3721,6 +3874,7 @@ int main(int argc, char ** argv) { test_msgs_oaicompat_json_conversion(); test_tools_oaicompat_json_conversion(); test_template_output_parsers(); + test_template_output_peg_parsers(); std::cout << "\n[chat] All tests passed!" << '\n'; } return 0; diff --git a/tests/test-grammar-integration.cpp b/tests/test-grammar-integration.cpp index 82fae671ed..7aa7e58a5c 100644 --- a/tests/test-grammar-integration.cpp +++ b/tests/test-grammar-integration.cpp @@ -32,13 +32,66 @@ static bool test_build_grammar_fails(const std::string & grammar_str) { return grammar_fails; } +struct token_and_piece { + llama_token token; + std::string piece; +}; + +// token() encodes a 32-bit ID as 5 bytes: a 0xff marker followed by the ID in big-endian order. +static std::string token(llama_token id) { + return std::string{ + static_cast(0xff), + static_cast((id >> 24) & 0xff), + static_cast((id >> 16) & 0xff), + static_cast((id >> 8) & 0xff), + static_cast(id & 0xff) + }; +} + +// parse_tokens() parses the token encodes above and UTF-8 text. +static std::vector parse_tokens(const std::string & input) { + std::vector result; + result.reserve(input.size()); + size_t offset = 0; + while (offset < input.size()) { + try { + if (static_cast(input[offset]) == 0xff) { + if (offset + 5 > input.size()) { + throw std::runtime_error("not enough bytes for token id"); + } + uint32_t val = + (static_cast(input[offset + 1]) << 24) | + (static_cast(input[offset + 2]) << 16) | + (static_cast(input[offset + 3]) << 8) | + (static_cast(input[offset + 4])); + auto piece = "<[" + std::to_string(val) + "]>"; + result.push_back({static_cast(val), piece}); + offset += 5; + } else { + uint32_t cpt = unicode_cpt_from_utf8(input, offset); + result.push_back({0, unicode_cpt_to_utf8(cpt)}); + } + } catch (const std::invalid_argument & /*ex*/) { + // Silently ignore invalid UTF-8 input to avoid leaking the exception beyond llama_tokenize + ++offset; + result.push_back({0, unicode_cpt_to_utf8(0xFFFD)}); // replacement character + } + } + return result; +} + static bool match_string(const std::string & input, llama_grammar * grammar) { - const auto cpts = unicode_cpts_from_utf8(input); + const auto parsed = parse_tokens(input); auto & stacks_cur = llama_grammar_get_stacks(grammar); - for (const auto & cpt : cpts) { - llama_grammar_accept(grammar, cpt); + for (const auto & in : parsed) { + try { + llama_grammar_accept_token(*grammar, in.token, in.piece); + } catch (const std::runtime_error & /*e*/) { + // normally this shouldn't get hit because of llama_grammar_apply + return false; + } if (stacks_cur.empty()) { // no stacks means that the grammar failed to match at this point @@ -426,6 +479,30 @@ static void test_simple_grammar() { "12a45", } ); + + // Test case for a simple grammar with tokens + test_grammar( + "simple grammar with tokens", + R"""( + root ::= <[10]> content <[11]> + content ::= (!<[11]>)*)""", + // Passing strings + { + token(10) + "hello world" + token(11), + token(10) + "text with " + token(12) + " other tokens " + token(13) + " mixed in" + token(11), + token(10) + token(11), + token(10) + token(12) + token(13) + token(14) + token(15) + token(11), + token(10) + "a" + token(11), + }, + // Failing strings + { + token(10) + "missing end token", + token(10), + "missing start token" + token(11), + token(10) + token(11) + token(11), // double end token + token(11) + "wrong order" + token(10), + } + ); } static void test_complex_grammar() { @@ -487,6 +564,34 @@ static void test_complex_grammar() { "123+456*789-123/456+789*123-456/789+123*456-789/123+456*789-123/456+789*123-456/", } ); + + // Test case for a more complex grammar with tokens + test_grammar( + "complex grammar with tokens", + R"""( + root ::= reasoning+ content tool-call* + reasoning ::= <[10]> (!<[11]>)* <[11]> + content ::= <[20]> (!<[21]>)* <[21]> + tool-call ::= <[12]> name <[13]> args <[14]> + name ::= (!<[13]>)+ + args ::= (!<[14]>)*)""", + // Passing strings + { + token(10) + "I am thinking" + token(11) + token(20) + "hello world!" + token(21) + token(12) + "search" + token(13) + "query=test" + token(14), + token(10) + "reasoning 1" + token(11) + token(10) + "reasoning 2" + token(11) + token(20) + token(21) + token(12) + "tool" + token(13) + token(14), + token(10) + token(11) + token(20) + "content" + token(21), + token(10) + "think" + token(12) + " nested" + token(11) + token(20) + token(10) + "more content" + token(21) + token(12) + "fn" + token(13) + "x=1,y=2" + token(14) + token(12) + "fn2" + token(13) + token(14), + token(10) + "reasoning" + token(11) + token(10) + "more" + token(11) + token(10) + "even more" + token(11) + token(20) + "text" + token(21) + token(12) + "a" + token(13) + "b" + token(14) + token(12) + "c" + token(13) + "d" + token(14), + }, + // Failing strings + { + token(20) + "content only" + token(21), + token(10) + "no closing reasoning", + token(10) + token(11) + token(20) + "no closing content", + token(10) + token(11) + token(20) + token(21) + token(12) + "incomplete tool", + token(10) + token(11) + token(11) + token(20) + token(21), + } + ); } static void test_special_chars() { diff --git a/tests/test-grammar-parser.cpp b/tests/test-grammar-parser.cpp index 67821a2d5c..03ae78ff73 100644 --- a/tests/test-grammar-parser.cpp +++ b/tests/test-grammar-parser.cpp @@ -515,5 +515,19 @@ int main() {LLAMA_GRETYPE_END, 0}, }); + // <[1000]> = "" + // <[1001]> = "" + verify_parsing(R"""( + root ::= <[1000]> !<[1001]> <[1001]> + )""", { + {"root", 0} + }, { + // root (index 0) + {LLAMA_GRETYPE_TOKEN, 1000}, + {LLAMA_GRETYPE_TOKEN_NOT, 1001}, + {LLAMA_GRETYPE_TOKEN, 1001}, + {LLAMA_GRETYPE_END, 0}, + }); + return 0; } diff --git a/tests/test-llama-grammar.cpp b/tests/test-llama-grammar.cpp index cc198f3e3c..fd45d5ada8 100644 --- a/tests/test-llama-grammar.cpp +++ b/tests/test-llama-grammar.cpp @@ -202,7 +202,7 @@ int main() uint32_t *cp = new uint32_t[2]; // dynamically allocate memory for code_point cp[0] = 37 + i; cp[1] = 0; - next_candidates[i] = {i, cp, {}}; + next_candidates[i] = {i, cp, {}, 0}; } std::vector>> expected_reject = { diff --git a/tests/test-lora-conversion-inference.sh b/tests/test-lora-conversion-inference.sh index 0255494b82..e7d6776933 100755 --- a/tests/test-lora-conversion-inference.sh +++ b/tests/test-lora-conversion-inference.sh @@ -79,19 +79,19 @@ run_conversion_and_inference_lora() { # Run inference echo -e "\n\n---------------------------\n\n" - echo "Running llama-cli without lora for $model_name with hidden_size $hidden_size..." - OUTPUT_BASE=$(./llama-cli -no-cnv -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32.gguf \ + echo "Running llama-completion without lora for $model_name with hidden_size $hidden_size..." + OUTPUT_BASE=$(./llama-completion -no-cnv -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32.gguf \ -p "$EXPECTED_BASE_FIRST_WORD" -n 50 --seed 42 --temp 0) echo -e "\n\n---------------------------\n\n" - echo "Running llama-cli with hot lora for $model_name with hidden_size $hidden_size..." - OUTPUT_LORA_HOT=$(./llama-cli -no-cnv -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32.gguf \ + echo "Running llama-completion with hot lora for $model_name with hidden_size $hidden_size..." + OUTPUT_LORA_HOT=$(./llama-completion -no-cnv -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32.gguf \ --lora $MODELS_REPO/$model_name/hidden_size=$hidden_size/lora/Lora-F32-LoRA.gguf \ -p "$EXPECTED_LORA_FIRST_WORD" -n 50 --seed 42 --temp 0) echo -e "\n\n---------------------------\n\n" - echo "Running llama-cli with merged lora for $model_name with hidden_size $hidden_size..." - OUTPUT_LORA_MERGED=$(./llama-cli -no-cnv -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32-lora-merged.gguf \ + echo "Running llama-completion with merged lora for $model_name with hidden_size $hidden_size..." + OUTPUT_LORA_MERGED=$(./llama-completion -no-cnv -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32-lora-merged.gguf \ -p "$EXPECTED_LORA_FIRST_WORD" -n 50 --seed 42 --temp 0) # Remove any initial white space diff --git a/tests/test-state-restore-fragmented.cpp b/tests/test-state-restore-fragmented.cpp new file mode 100644 index 0000000000..481b39d04c --- /dev/null +++ b/tests/test-state-restore-fragmented.cpp @@ -0,0 +1,122 @@ +// Test for state restore with fragmented KV cache +// This tests the fix for: https://github.com/ggml-org/llama.cpp/issues/17527 +// The issue was that state restore required contiguous KV cache slots, +// which fails when the cache is fragmented. +// +// The fix changes find_slot(ubatch, true) to find_slot(ubatch, false) +// in state_read_meta(), allowing non-contiguous slot allocation. + +#include "arg.h" +#include "common.h" +#include "llama.h" + +#include +#include +#include + +int main(int argc, char ** argv) { + common_params params; + + params.sampling.seed = 1234; + params.kv_unified = true; + params.n_parallel = 3; + params.n_ctx = 256; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) { + return 1; + } + + common_init(); + + // init + common_init_result_ptr llama_init = common_init_from_params(params); + + llama_model * model = llama_init->model(); + llama_context * ctx = llama_init->context(); + + if (model == nullptr || ctx == nullptr) { + fprintf(stderr, "%s : failed to init\n", __func__); + return 1; + } + + GGML_UNUSED(model); + + // tokenize prompt + std::vector tokens(70, 1); + + // interleave the 3 sequences: + // 01201230123... + llama_batch batch = llama_batch_init(params.n_parallel*tokens.size(), 0, 1); + for (size_t i = 0; i < tokens.size(); i++) { + for (int s = 0; s < params.n_parallel; ++s) { + common_batch_add(batch, tokens[i], i, {s}, false); + } + } + batch.logits[batch.n_tokens - 1] = true; + + if (llama_decode(ctx, batch)) { + fprintf(stderr, "%s : failed to decode seq 0\n", __func__); + return 1; + } + + fprintf(stderr, "%s : processed prompt on seq 0, 1, 2 (%zu tokens each)\n", __func__, tokens.size()); + + // Save state of seq 1 + std::vector seq_state(llama_state_seq_get_size(ctx, 1)); + const size_t ncopy = llama_state_seq_get_data(ctx, seq_state.data(), seq_state.size(), 1); + if (ncopy != seq_state.size()) { + fprintf(stderr, "%s : failed to save seq 1 state\n", __func__); + return 1; + } + fprintf(stderr, "%s : saved seq 1 state, %zu bytes\n", __func__, ncopy); + + // clear seq 1 to create a "hole" in the KV cache (fragmentation) + // 0.20.20.20.2.... + llama_memory_t mem = llama_get_memory(ctx); + llama_memory_seq_rm(mem, 1, -1, -1); + fprintf(stderr, "%s : cleared seq 1 to create fragmentation\n", __func__); + + // Now the cache has holes where seq 1 was + // This creates fragmentation - there's no contiguous block large enough + // for the seq 1 state if we only look for contiguous slots + + // Restore seq 1 state into seq 1 (should work with non-contiguous allocation) + // We use seq 1 since it's a valid sequence ID (0 to n_parallel-1) + // Before the fix, this would fail with "failed to find available cells in kv cache" + const size_t nset = llama_state_seq_set_data(ctx, seq_state.data(), seq_state.size(), 1); + if (nset != seq_state.size()) { + fprintf(stderr, "%s : FAILED to restore seq state into fragmented cache (got %zu, expected %zu)\n", + __func__, nset, seq_state.size()); + fprintf(stderr, "%s : This is the bug - state restore fails with fragmented KV cache\n", __func__); + llama_batch_free(batch); + return 1; + } + fprintf(stderr, "%s : restored state into seq 1, %zu bytes\n", __func__, nset); + + // Verify we can decode with the restored state + // Generate one token to verify the restored state is usable + auto sparams = llama_sampler_chain_default_params(); + llama_sampler * smpl = llama_sampler_chain_init(sparams); + llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sampling.seed)); + + auto next_token = llama_sampler_sample(smpl, ctx, -1); + auto next_token_str = common_token_to_piece(ctx, next_token); + + common_batch_clear(batch); + common_batch_add(batch, next_token, (int)tokens.size(), {1}, true); + + if (llama_decode(ctx, batch)) { + fprintf(stderr, "%s : failed to decode with restored state\n", __func__); + llama_sampler_free(smpl); + llama_batch_free(batch); + return 1; + } + + fprintf(stderr, "%s : successfully decoded with restored state, generated: '%s'\n", __func__, next_token_str.c_str()); + fprintf(stderr, "%s : SUCCESS - state restore works with fragmented KV cache\n", __func__); + + llama_sampler_free(smpl); + llama_batch_free(batch); + + return 0; +} diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt index d64956b843..8df3f41003 100644 --- a/tools/CMakeLists.txt +++ b/tools/CMakeLists.txt @@ -18,7 +18,8 @@ else() add_subdirectory(gguf-split) add_subdirectory(imatrix) add_subdirectory(llama-bench) - add_subdirectory(main) + add_subdirectory(cli) + add_subdirectory(completion) add_subdirectory(perplexity) add_subdirectory(quantize) if (LLAMA_BUILD_SERVER) @@ -36,4 +37,5 @@ else() add_subdirectory(cvector-generator) add_subdirectory(export-lora) endif() + add_subdirectory(fit-params) endif() diff --git a/tools/cli/CMakeLists.txt b/tools/cli/CMakeLists.txt new file mode 100644 index 0000000000..b08fff4c28 --- /dev/null +++ b/tools/cli/CMakeLists.txt @@ -0,0 +1,10 @@ +set(TARGET llama-cli) +add_executable(${TARGET} cli.cpp) +target_link_libraries(${TARGET} PRIVATE server-context PUBLIC common ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) + +include_directories(../server) + +if(LLAMA_TOOLS_INSTALL) + install(TARGETS ${TARGET} RUNTIME) +endif() diff --git a/tools/cli/README.md b/tools/cli/README.md new file mode 100644 index 0000000000..1333ed77b7 --- /dev/null +++ b/tools/cli/README.md @@ -0,0 +1 @@ +TODO diff --git a/tools/cli/cli.cpp b/tools/cli/cli.cpp new file mode 100644 index 0000000000..8a8639207b --- /dev/null +++ b/tools/cli/cli.cpp @@ -0,0 +1,395 @@ +#include "common.h" +#include "arg.h" +#include "console.h" +// #include "log.h" + +#include "server-context.h" +#include "server-task.h" + +#include +#include +#include +#include + +#if defined(_WIN32) +#define WIN32_LEAN_AND_MEAN +#ifndef NOMINMAX +# define NOMINMAX +#endif +#include +#endif + +const char * LLAMA_ASCII_LOGO = R"( +▄▄ ▄▄ +██ ██ +██ ██ ▀▀█▄ ███▄███▄ ▀▀█▄ ▄████ ████▄ ████▄ +██ ██ ▄█▀██ ██ ██ ██ ▄█▀██ ██ ██ ██ ██ ██ +██ ██ ▀█▄██ ██ ██ ██ ▀█▄██ ██ ▀████ ████▀ ████▀ + ██ ██ + ▀▀ ▀▀ +)"; + +static std::atomic g_is_interrupted = false; +static bool should_stop() { + return g_is_interrupted.load(); +} + +#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) +static void signal_handler(int) { + if (g_is_interrupted.load()) { + // second Ctrl+C - exit immediately + // make sure to clear colors before exiting (not using LOG or console.cpp here to avoid deadlock) + fprintf(stdout, "\033[0m\n"); + fflush(stdout); + std::exit(130); + } + g_is_interrupted.store(true); +} +#endif + +struct cli_context { + server_context ctx_server; + json messages = json::array(); + std::vector input_files; + task_params defaults; + + // thread for showing "loading" animation + std::atomic loading_show; + + cli_context(const common_params & params) { + defaults.sampling = params.sampling; + defaults.speculative = params.speculative; + defaults.n_keep = params.n_keep; + defaults.n_predict = params.n_predict; + defaults.antiprompt = params.antiprompt; + + defaults.stream = true; // make sure we always use streaming mode + defaults.timings_per_token = true; // in order to get timings even when we cancel mid-way + // defaults.return_progress = true; // TODO: show progress + defaults.oaicompat_chat_syntax.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; + } + + std::string generate_completion(result_timings & out_timings) { + server_response_reader rd = ctx_server.get_response_reader(); + { + // TODO: reduce some copies here in the future + server_task task = server_task(SERVER_TASK_TYPE_COMPLETION); + task.id = rd.get_new_id(); + task.index = 0; + task.params = defaults; // copy + task.cli_input = messages; // copy + task.cli_files = input_files; // copy + rd.post_task({std::move(task)}); + } + + // wait for first result + console::spinner::start(); + server_task_result_ptr result = rd.next(should_stop); + + console::spinner::stop(); + std::string curr_content; + bool is_thinking = false; + + while (result) { + if (should_stop()) { + break; + } + if (result->is_error()) { + json err_data = result->to_json(); + if (err_data.contains("message")) { + console::error("Error: %s\n", err_data["message"].get().c_str()); + } else { + console::error("Error: %s\n", err_data.dump().c_str()); + } + return curr_content; + } + auto res_partial = dynamic_cast(result.get()); + if (res_partial) { + out_timings = std::move(res_partial->timings); + for (const auto & diff : res_partial->oaicompat_msg_diffs) { + if (!diff.content_delta.empty()) { + if (is_thinking) { + console::log("\n[End thinking]\n\n"); + console::set_display(DISPLAY_TYPE_RESET); + is_thinking = false; + } + curr_content += diff.content_delta; + console::log("%s", diff.content_delta.c_str()); + console::flush(); + } + if (!diff.reasoning_content_delta.empty()) { + console::set_display(DISPLAY_TYPE_REASONING); + if (!is_thinking) { + console::log("[Start thinking]\n"); + } + is_thinking = true; + console::log("%s", diff.reasoning_content_delta.c_str()); + console::flush(); + } + } + } + auto res_final = dynamic_cast(result.get()); + if (res_final) { + out_timings = std::move(res_final->timings); + break; + } + result = rd.next(should_stop); + } + g_is_interrupted.store(false); + // server_response_reader automatically cancels pending tasks upon destruction + return curr_content; + } + + // TODO: support remote files in the future (http, https, etc) + std::string load_input_file(const std::string & fname, bool is_media) { + std::ifstream file(fname, std::ios::binary); + if (!file) { + return ""; + } + if (is_media) { + raw_buffer buf; + buf.assign((std::istreambuf_iterator(file)), std::istreambuf_iterator()); + input_files.push_back(std::move(buf)); + return mtmd_default_marker(); + } else { + std::string content((std::istreambuf_iterator(file)), std::istreambuf_iterator()); + return content; + } + } +}; + +int main(int argc, char ** argv) { + common_params params; + + params.verbosity = LOG_LEVEL_ERROR; // by default, less verbose logs + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_CLI)) { + return 1; + } + + // TODO: maybe support it later? + if (params.conversation_mode == COMMON_CONVERSATION_MODE_DISABLED) { + console::error("--no-conversation is not supported by llama-cli\n"); + console::error("please use llama-completion instead\n"); + } + + common_init(); + + // struct that contains llama context and inference + cli_context ctx_cli(params); + + llama_backend_init(); + llama_numa_init(params.numa); + + // TODO: avoid using atexit() here by making `console` a singleton + console::init(params.simple_io, params.use_color); + atexit([]() { console::cleanup(); }); + + console::set_display(DISPLAY_TYPE_RESET); + +#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) + struct sigaction sigint_action; + sigint_action.sa_handler = signal_handler; + sigemptyset (&sigint_action.sa_mask); + sigint_action.sa_flags = 0; + sigaction(SIGINT, &sigint_action, NULL); + sigaction(SIGTERM, &sigint_action, NULL); +#elif defined (_WIN32) + auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL { + return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false; + }; + SetConsoleCtrlHandler(reinterpret_cast(console_ctrl_handler), true); +#endif + + console::log("\nLoading model... "); // followed by loading animation + console::spinner::start(); + if (!ctx_cli.ctx_server.load_model(params)) { + console::spinner::stop(); + console::error("\nFailed to load the model\n"); + return 1; + } + + ctx_cli.ctx_server.init(); + + console::spinner::stop(); + console::log("\n"); + + std::thread inference_thread([&ctx_cli]() { + ctx_cli.ctx_server.start_loop(); + }); + + auto inf = ctx_cli.ctx_server.get_info(); + std::string modalities = "text"; + if (inf.has_inp_image) { + modalities += ", vision"; + } + if (inf.has_inp_audio) { + modalities += ", audio"; + } + + if (!params.system_prompt.empty()) { + ctx_cli.messages.push_back({ + {"role", "system"}, + {"content", params.system_prompt} + }); + } + + console::log("\n"); + console::log("%s\n", LLAMA_ASCII_LOGO); + console::log("build : %s\n", inf.build_info.c_str()); + console::log("model : %s\n", inf.model_name.c_str()); + console::log("modalities : %s\n", modalities.c_str()); + if (!params.system_prompt.empty()) { + console::log("using custom system prompt\n"); + } + console::log("\n"); + console::log("available commands:\n"); + console::log(" /exit or Ctrl+C stop or exit\n"); + console::log(" /regen regenerate the last response\n"); + console::log(" /clear clear the chat history\n"); + console::log(" /read add a text file\n"); + if (inf.has_inp_image) { + console::log(" /image add an image file\n"); + } + if (inf.has_inp_audio) { + console::log(" /audio add an audio file\n"); + } + console::log("\n"); + + // interactive loop + std::string cur_msg; + while (true) { + std::string buffer; + console::set_display(DISPLAY_TYPE_USER_INPUT); + if (params.prompt.empty()) { + console::log("\n> "); + std::string line; + bool another_line = true; + do { + another_line = console::readline(line, params.multiline_input); + buffer += line; + } while (another_line); + } else { + // process input prompt from args + for (auto & fname : params.image) { + std::string marker = ctx_cli.load_input_file(fname, true); + if (marker.empty()) { + console::error("file does not exist or cannot be opened: '%s'\n", fname.c_str()); + break; + } + console::log("Loaded media from '%s'\n", fname.c_str()); + cur_msg += marker; + } + buffer = params.prompt; + if (buffer.size() > 500) { + console::log("\n> %s ... (truncated)\n", buffer.substr(0, 500).c_str()); + } else { + console::log("\n> %s\n", buffer.c_str()); + } + params.prompt.clear(); // only use it once + } + console::set_display(DISPLAY_TYPE_RESET); + console::log("\n"); + + if (should_stop()) { + g_is_interrupted.store(false); + break; + } + + // remove trailing newline + if (!buffer.empty() &&buffer.back() == '\n') { + buffer.pop_back(); + } + + // skip empty messages + if (buffer.empty()) { + continue; + } + + bool add_user_msg = true; + + // process commands + if (string_starts_with(buffer, "/exit")) { + break; + } else if (string_starts_with(buffer, "/regen")) { + if (ctx_cli.messages.size() >= 2) { + size_t last_idx = ctx_cli.messages.size() - 1; + ctx_cli.messages.erase(last_idx); + add_user_msg = false; + } else { + console::error("No message to regenerate.\n"); + continue; + } + } else if (string_starts_with(buffer, "/clear")) { + ctx_cli.messages.clear(); + ctx_cli.input_files.clear(); + console::log("Chat history cleared.\n"); + continue; + } else if ( + (string_starts_with(buffer, "/image ") && inf.has_inp_image) || + (string_starts_with(buffer, "/audio ") && inf.has_inp_audio)) { + // just in case (bad copy-paste for example), we strip all trailing/leading spaces + std::string fname = string_strip(buffer.substr(7)); + std::string marker = ctx_cli.load_input_file(fname, true); + if (marker.empty()) { + console::error("file does not exist or cannot be opened: '%s'\n", fname.c_str()); + continue; + } + cur_msg += marker; + console::log("Loaded media from '%s'\n", fname.c_str()); + continue; + } else if (string_starts_with(buffer, "/read ")) { + std::string fname = string_strip(buffer.substr(6)); + std::string marker = ctx_cli.load_input_file(fname, false); + if (marker.empty()) { + console::error("file does not exist or cannot be opened: '%s'\n", fname.c_str()); + continue; + } + cur_msg += marker; + console::log("Loaded text from '%s'\n", fname.c_str()); + continue; + } else { + // not a command + cur_msg += buffer; + } + + // generate response + if (add_user_msg) { + ctx_cli.messages.push_back({ + {"role", "user"}, + {"content", cur_msg} + }); + cur_msg.clear(); + } + result_timings timings; + std::string assistant_content = ctx_cli.generate_completion(timings); + ctx_cli.messages.push_back({ + {"role", "assistant"}, + {"content", assistant_content} + }); + console::log("\n"); + + if (params.show_timings) { + console::set_display(DISPLAY_TYPE_INFO); + console::log("\n"); + console::log("[ Prompt: %.1f t/s | Generation: %.1f t/s ]\n", timings.prompt_per_second, timings.predicted_per_second); + console::set_display(DISPLAY_TYPE_RESET); + } + + if (params.single_turn) { + break; + } + } + + console::set_display(DISPLAY_TYPE_RESET); + + console::log("\nExiting...\n"); + ctx_cli.ctx_server.terminate(); + inference_thread.join(); + + // bump the log level to display timings + common_log_set_verbosity_thold(LOG_LEVEL_INFO); + llama_memory_breakdown_print(ctx_cli.ctx_server.get_llama_context()); + + return 0; +} diff --git a/tools/main/CMakeLists.txt b/tools/completion/CMakeLists.txt similarity index 74% rename from tools/main/CMakeLists.txt rename to tools/completion/CMakeLists.txt index 8f8e9d444c..126ae6ab3d 100644 --- a/tools/main/CMakeLists.txt +++ b/tools/completion/CMakeLists.txt @@ -1,5 +1,5 @@ -set(TARGET llama-cli) -add_executable(${TARGET} main.cpp) +set(TARGET llama-completion) +add_executable(${TARGET} completion.cpp) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/tools/main/README.md b/tools/completion/README.md similarity index 95% rename from tools/main/README.md rename to tools/completion/README.md index 54e582de07..57ef394213 100644 --- a/tools/main/README.md +++ b/tools/completion/README.md @@ -1,4 +1,4 @@ -# llama.cpp/tools/main +# llama.cpp/tools/completion This example program allows you to use various LLaMA language models easily and efficiently. It is specifically designed to work with the [llama.cpp](https://github.com/ggml-org/llama.cpp) project, which provides a plain C/C++ implementation with optional 4-bit quantization support for faster, lower memory inference, and is optimized for desktop CPUs. This program can be used to perform various inference tasks with LLaMA models, including generating text based on user-provided prompts and chat-like interactions with reverse prompts. @@ -27,64 +27,64 @@ Once downloaded, place your model in the models folder in llama.cpp. ##### Input prompt (One-and-done) ```bash -./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time" +./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time" ``` ##### Conversation mode (Allow for continuous interaction with the model) ```bash -./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma +./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma ``` ##### Conversation mode using built-in jinja chat template ```bash -./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja +./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja ``` ##### One-and-done query using jinja with custom system prompt and a starting prompt ```bash -./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello" +./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello" ``` ##### Infinite text from a starting prompt (you can use `Ctrl-C` to stop it): ```bash -./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 +./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 ``` ### Windows: ##### Input prompt (One-and-done) ```powershell -./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time" +./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time" ``` ##### Conversation mode (Allow for continuous interaction with the model) ```powershell -./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma +./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma ``` ##### Conversation mode using built-in jinja chat template ```powershell -./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja +./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja ``` ##### One-and-done query using jinja with custom system prompt and a starting prompt ```powershell -./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello" +./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello" ``` #### Infinite text from a starting prompt (you can use `Ctrl-C` to stop it): ```powershell -llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 +llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 ``` ## Common Options -In this section, we cover the most commonly used options for running the `llama-cli` program with the LLaMA models: +In this section, we cover the most commonly used options for running the `llama-completion` program with the LLaMA models: - `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/gemma-1.1-7b-it.Q4_K_M.gguf`; inferred from `--model-url` if set). - `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g [https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true](https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true)). @@ -97,7 +97,7 @@ In this section, we cover the most commonly used options for running the `llama- ## Input Prompts -The `llama-cli` program provides several ways to interact with the LLaMA models using input prompts: +The `llama-completion` program provides several ways to interact with the LLaMA models using input prompts: - `--prompt PROMPT`: Provide a prompt directly as a command-line option. - `--file FNAME`: Provide a file containing a prompt or multiple prompts. @@ -107,7 +107,7 @@ The `llama-cli` program provides several ways to interact with the LLaMA models ## Interaction -The `llama-cli` program offers a seamless way to interact with LLaMA models, allowing users to engage in real-time conversations or provide instructions for specific tasks. The interactive mode can be triggered using various options, including `--interactive` and `--interactive-first`. +The `llama-completion` program offers a seamless way to interact with LLaMA models, allowing users to engage in real-time conversations or provide instructions for specific tasks. The interactive mode can be triggered using various options, including `--interactive` and `--interactive-first`. In interactive mode, users can participate in text generation by injecting their input during the process. Users can press `Ctrl+C` at any time to interject and type their input, followed by pressing `Return` to submit it to the LLaMA model. To submit additional lines without finalizing input, users can end the current line with a backslash (`\`) and continue typing. @@ -136,7 +136,7 @@ To overcome this limitation, you can use the `--in-prefix` flag to add a space o The `--in-prefix` flag is used to add a prefix to your input, primarily, this is used to insert a space after the reverse prompt. Here's an example of how to use the `--in-prefix` flag in conjunction with the `--reverse-prompt` flag: ```sh -./llama-cli -r "User:" --in-prefix " " +./llama-completion -r "User:" --in-prefix " " ``` ### In-Suffix @@ -144,7 +144,7 @@ The `--in-prefix` flag is used to add a prefix to your input, primarily, this is The `--in-suffix` flag is used to add a suffix after your input. This is useful for adding an "Assistant:" prompt after the user's input. It's added after the new-line character (`\n`) that's automatically added to the end of the user's input. Here's an example of how to use the `--in-suffix` flag in conjunction with the `--reverse-prompt` flag: ```sh -./llama-cli -r "User:" --in-prefix " " --in-suffix "Assistant:" +./llama-completion -r "User:" --in-prefix " " --in-suffix "Assistant:" ``` When --in-prefix or --in-suffix options are enabled the chat template ( --chat-template ) is disabled diff --git a/tools/main/main.cpp b/tools/completion/completion.cpp similarity index 97% rename from tools/main/main.cpp rename to tools/completion/completion.cpp index 960ddbe391..85480f3369 100644 --- a/tools/main/main.cpp +++ b/tools/completion/completion.cpp @@ -86,7 +86,11 @@ static void sigint_handler(int signo) { int main(int argc, char ** argv) { common_params params; g_params = ¶ms; - if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_MAIN, print_usage)) { + + // disable jinja by default + params.use_jinja = false; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMPLETION, print_usage)) { return 1; } @@ -137,13 +141,15 @@ int main(int argc, char ** argv) { // load the model and apply lora adapter, if any LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__); - common_init_result llama_init = common_init_from_params(params); - model = llama_init.model.get(); - ctx = llama_init.context.get(); + auto llama_init = common_init_from_params(params); - if (model == NULL) { - LOG_ERR("%s: error: unable to load model\n", __func__); + ctx = llama_init->context(); + model = llama_init->model(); + smpl = llama_init->sampler(0); + + if (ctx == NULL) { + LOG_ERR("%s: error: unable to create context\n", __func__); return 1; } @@ -470,12 +476,6 @@ int main(int argc, char ** argv) { } } - smpl = common_sampler_init(model, sparams); - if (!smpl) { - LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__); - return 1; - } - LOG_INF("sampler seed: %u\n", common_sampler_get_seed(smpl)); LOG_INF("sampler params: \n%s\n", sparams.print().c_str()); LOG_INF("sampler chain: %s\n", common_sampler_print(smpl).c_str()); @@ -521,12 +521,6 @@ int main(int argc, char ** argv) { is_interacting = params.interactive_first; } - LOG_WRN("*****************************\n"); - LOG_WRN("IMPORTANT: The current llama-cli will be moved to llama-completion in the near future\n"); - LOG_WRN(" New llama-cli will have enhanced features and improved user experience\n"); - LOG_WRN(" More info: https://github.com/ggml-org/llama.cpp/discussions/17618\n"); - LOG_WRN("*****************************\n"); - bool is_antiprompt = false; bool input_echo = true; bool display = true; @@ -543,7 +537,7 @@ int main(int argc, char ** argv) { std::ostringstream assistant_ss; // for storing current assistant message, used in conversation mode // the first thing we will do is to output the prompt, so set color accordingly - console::set_display(console::prompt); + console::set_display(DISPLAY_TYPE_PROMPT); display = params.display_prompt; std::vector embd; @@ -588,9 +582,9 @@ int main(int argc, char ** argv) { const int skipped_tokens = (int) embd.size() - max_embd_size; embd.resize(max_embd_size); - console::set_display(console::error); + console::set_display(DISPLAY_TYPE_ERROR); LOG_WRN("<>", skipped_tokens, skipped_tokens != 1 ? "s" : ""); - console::set_display(console::reset); + console::set_display(DISPLAY_TYPE_RESET); } if (ga_n == 1) { @@ -772,7 +766,7 @@ int main(int argc, char ** argv) { // reset color to default if there is no pending user input if (input_echo && (int) embd_inp.size() == n_consumed) { - console::set_display(console::reset); + console::set_display(DISPLAY_TYPE_RESET); display = true; } @@ -868,7 +862,7 @@ int main(int argc, char ** argv) { } // color user input only - console::set_display(console::user_input); + console::set_display(DISPLAY_TYPE_USER_INPUT); display = params.display_prompt; std::string line; @@ -879,7 +873,7 @@ int main(int argc, char ** argv) { } while (another_line); // done taking input, reset color - console::set_display(console::reset); + console::set_display(DISPLAY_TYPE_RESET); display = true; if (buffer.empty()) { // Ctrl+D on empty line exits @@ -995,8 +989,6 @@ int main(int argc, char ** argv) { LOG("\n\n"); common_perf_print(ctx, smpl); - common_sampler_free(smpl); - llama_backend_free(); ggml_threadpool_free_fn(threadpool); diff --git a/tools/cvector-generator/cvector-generator.cpp b/tools/cvector-generator/cvector-generator.cpp index d2d97e05ce..3ba7c52950 100644 --- a/tools/cvector-generator/cvector-generator.cpp +++ b/tools/cvector-generator/cvector-generator.cpp @@ -419,10 +419,10 @@ int main(int argc, char ** argv) { llama_numa_init(params.numa); // load the model to get hparams - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); // int n_ctx = llama_n_ctx(ctx); int n_layers = llama_model_n_layer(model); diff --git a/tools/fit-params/CMakeLists.txt b/tools/fit-params/CMakeLists.txt new file mode 100644 index 0000000000..34c3373f83 --- /dev/null +++ b/tools/fit-params/CMakeLists.txt @@ -0,0 +1,8 @@ +set(TARGET llama-fit-params) +add_executable(${TARGET} fit-params.cpp) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) + +if(LLAMA_TOOLS_INSTALL) + install(TARGETS ${TARGET} RUNTIME) +endif() diff --git a/tools/fit-params/README.md b/tools/fit-params/README.md new file mode 100644 index 0000000000..8f0c958a2f --- /dev/null +++ b/tools/fit-params/README.md @@ -0,0 +1,55 @@ +# fit-params + +llama.cpp binaries can automatically fit the projected memory use of a model to the free device memory available at runtime, +this is controlled using the CLI arguments starting with `-fit`/`--fit`. +Internally the code is calling `llama_params_fit` to adjust the `llama_model_params` and `llama_context_params` structs. +`llama-fit-params` is a simple utility that prints the CLI arguments corresponding to these adjustments to stdout. +Example usage: + +``` bash +# First, run llama-fit-params and store the results in a file: +> ./build/bin/llama-fit-params --model /opt/models/qwen_3-30b3a-f16.gguf | tee args.txt +ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no +ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no +ggml_cuda_init: found 1 CUDA devices: + Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes +build: 6895 (4341dc8bc) with cc (GCC) 15.2.1 20250813 for x86_64-pc-linux-gnu +llama_params_fit_impl: projected to use 61807 MiB of device memory vs. 24077 MiB of free device memory +llama_params_fit_impl: cannot fulfill margin of 1024 MiB, need to reduce device memory by 42444 MiB +llama_params_fit_impl: context size reduced from 40960 to 4096 -> need 3456 MiB less memory in total +llama_params_fit_impl: with only dense weights in device memory there is a total surplus of 16164 MiB +llama_params_fit_impl: distributing layers across devices with overflow to next device/system memory: +llama_params_fit_impl: - CUDA0 (NVIDIA GeForce RTX 4090): 48 layers (34 overflowing), 19187 MiB used, 1199 MiB free +llama_params_fit: successfully fit params to free device memory +llama_params_fit: fitting params to free memory took 1.15 seconds +Printing fitted CLI arguments to stdout... +-c 4096 -ngl 48 -ot blk\.14\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.15\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.16\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.17\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.18\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.19\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.20\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.21\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.22\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.23\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.24\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.25\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.26\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.27\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.28\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.29\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.30\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.31\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.32\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.33\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.34\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.35\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.36\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.37\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.38\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.39\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.40\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.41\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.42\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.43\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.44\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.45\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.46\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.47\.ffn_(up|down|gate)_(ch|)exps=CPU + +# Next, use those results for a llama.cpp binary: +> cat args.txt | xargs ./build/bin/llama-server --model /opt/models/qwen_3-30b3a-f16.gguf +ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no +ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no +ggml_cuda_init: found 1 CUDA devices: + Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes +build: 6895 (4341dc8bc) with cc (GCC) 15.2.1 20250813 for x86_64-pc-linux-gnu +system info: n_threads = 16, n_threads_batch = 16, total_threads = 32 + +system_info: n_threads = 16 (n_threads_batch = 16) / 32 | CUDA : ARCHS = 890 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX_VNNI = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 | + +main: binding port with default address family +main: HTTP server is listening, hostname: 127.0.0.1, port: 8080, http threads: 31 +main: loading model +srv load_model: loading model '/opt/models/qwen_3-30b3a-f16.gguf' +llama_params_fit_impl: projected to use 19187 MiB of device memory vs. 24077 MiB of free device memory +llama_params_fit_impl: will leave 1199 >= 1024 MiB of free device memory, no changes needed +llama_params_fit: successfully fit params to free device memory +llama_params_fit: fitting params to free memory took 0.28 seconds +[...] +main: server is listening on http://127.0.0.1:8080 - starting the main loop +srv update_slots: all slots are idle +^Csrv operator(): operator(): cleaning up before exit... + +llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted | +llama_memory_breakdown_print: | - CUDA0 (RTX 4090) | 24077 = 945 + (19187 = 17904 + 384 + 898) + 3945 | +llama_memory_breakdown_print: | - Host | 58271 = 58259 + 0 + 12 | +``` diff --git a/tools/fit-params/fit-params.cpp b/tools/fit-params/fit-params.cpp new file mode 100644 index 0000000000..fbf7a2eb37 --- /dev/null +++ b/tools/fit-params/fit-params.cpp @@ -0,0 +1,62 @@ +#include "llama.h" + +#include "arg.h" +#include "common.h" +#include "log.h" + +#include + +#if defined(_MSC_VER) +#pragma warning(disable: 4244 4267) // possible loss of data +#endif + +int main(int argc, char ** argv) { + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) { + return 1; + } + + common_init(); + llama_backend_init(); + llama_numa_init(params.numa); + auto mparams = common_model_params_to_llama(params); + auto cparams = common_context_params_to_llama(params); + llama_params_fit(params.model.path.c_str(), &mparams, &cparams, + params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target, params.fit_params_min_ctx, + params.verbosity >= 4 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR); + + LOG_INF("Printing fitted CLI arguments to stdout...\n"); + std::cout << "-c " << cparams.n_ctx; + std::cout << " -ngl " << mparams.n_gpu_layers; + + size_t nd = llama_max_devices(); + while (nd > 1 && mparams.tensor_split[nd - 1] == 0.0f) { + nd--; + } + if (nd > 1) { + for (size_t id = 0; id < nd; id++) { + if (id == 0) { + std::cout << " -ts "; + } + if (id > 0) { + std::cout << ","; + } + std::cout << mparams.tensor_split[id]; + } + } + + const size_t ntbo = llama_max_tensor_buft_overrides(); + for (size_t itbo = 0; itbo < ntbo && mparams.tensor_buft_overrides[itbo].pattern != nullptr; itbo++) { + if (itbo == 0) { + std::cout << " -ot "; + } + if (itbo > 0) { + std::cout << ","; + } + std::cout << mparams.tensor_buft_overrides[itbo].pattern << "=" << ggml_backend_buft_name(mparams.tensor_buft_overrides[itbo].buft); + } + std::cout << "\n"; + + return 0; +} diff --git a/tools/gguf-split/tests.sh b/tools/gguf-split/tests.sh index e8677018f5..c8dd0b0079 100755 --- a/tools/gguf-split/tests.sh +++ b/tools/gguf-split/tests.sh @@ -19,7 +19,7 @@ fi set -x SPLIT=$1/llama-gguf-split -MAIN=$1/llama-cli +MAIN=$1/llama-completion WORK_PATH=$TMP_DIR/gguf-split ROOT_DIR=$(realpath $(dirname $0)/../../) diff --git a/tools/imatrix/imatrix.cpp b/tools/imatrix/imatrix.cpp index f28a036dee..669de55ddb 100644 --- a/tools/imatrix/imatrix.cpp +++ b/tools/imatrix/imatrix.cpp @@ -1265,10 +1265,10 @@ int main(int argc, char ** argv) { params.warmup = false; // init - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); if (model == nullptr || ctx == nullptr) { LOG_ERR("%s : failed to init\n", __func__); diff --git a/tools/llama-bench/README.md b/tools/llama-bench/README.md index 87d9c0a219..c837bb6d26 100644 --- a/tools/llama-bench/README.md +++ b/tools/llama-bench/README.md @@ -80,7 +80,7 @@ Each test is repeated the number of times given by `-r`, and the results are ave Using the `-d ` option, each test can be run at a specified context depth, prefilling the KV cache with `` tokens. -For a description of the other options, see the [main example](../main/README.md). +For a description of the other options, see the [completion example](../completion/README.md). > [!NOTE] > The measurements with `llama-bench` do not include the times for tokenization and for sampling. diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt index f640ae2a6e..3ee42036fd 100644 --- a/tools/mtmd/CMakeLists.txt +++ b/tools/mtmd/CMakeLists.txt @@ -6,16 +6,31 @@ add_library(mtmd mtmd.cpp mtmd-audio.cpp mtmd.h + mtmd-helper.cpp + mtmd-helper.h clip.cpp clip.h clip-impl.h - mtmd-helper.cpp - mtmd-helper.h + clip-model.h + clip-graph.h + models/models.h + models/cogvlm.cpp + models/internvl.cpp + models/kimivl.cpp + models/llama4.cpp + models/llava.cpp + models/minicpmv.cpp + models/pixtral.cpp + models/qwen2vl.cpp + models/qwen3vl.cpp + models/siglip.cpp + models/whisper-enc.cpp ) set_target_properties(mtmd PROPERTIES VERSION ${LLAMA_INSTALL_VERSION} SOVERSION 0 + MACHO_CURRENT_VERSION 0 # keep macOS linker from seeing oversized version number ) target_link_libraries (mtmd PUBLIC ggml llama) @@ -52,6 +67,15 @@ if (TARGET BUILD_INFO) add_dependencies(mtmd-helper BUILD_INFO) endif() +# if mtmd is linked against common, we throw an error +if (TARGET mtmd) + get_target_property(libs mtmd LINK_LIBRARIES) + if (libs AND "common" IN_LIST libs) + message(FATAL_ERROR "mtmd is designed to be a public library.\n" + "It must not link against common") + endif() +endif() + add_executable(llama-llava-cli deprecation-warning.cpp) add_executable(llama-gemma3-cli deprecation-warning.cpp) add_executable(llama-minicpmv-cli deprecation-warning.cpp) diff --git a/tools/mtmd/clip-graph.h b/tools/mtmd/clip-graph.h new file mode 100644 index 0000000000..17f90e8aa8 --- /dev/null +++ b/tools/mtmd/clip-graph.h @@ -0,0 +1,119 @@ +#pragma once + +#include "ggml.h" +#include "ggml-cpp.h" +#include "clip.h" +#include "clip-impl.h" +#include "clip-model.h" + +#include +#include + +struct clip_graph { + const clip_model & model; + const clip_hparams & hparams; + projector_type proj_type; + + // we only support single image per batch + const clip_image_f32 & img; + + const int patch_size; + const int n_patches_x; + const int n_patches_y; + const int n_patches; + const int n_embd; + const int n_head; + const int d_head; + const int n_layer; + const int n_mmproj_embd; + const float eps; + const float kq_scale; + const clip_flash_attn_type flash_attn_type; + + // for debugging + const bool debug_graph; + std::vector & debug_print_tensors; + + ggml_context_ptr ctx0_ptr; + ggml_context * ctx0; + ggml_cgraph * gf; + + clip_graph(clip_ctx * ctx, const clip_image_f32 & img); + + virtual ~clip_graph() = default; + virtual ggml_cgraph * build() = 0; + + // + // utility functions + // + void cb(ggml_tensor * cur0, const char * name, int il) const; + + // siglip2 naflex + ggml_tensor * resize_position_embeddings(); + + // build vision transformer (ViT) cgraph + // this function should cover most of the models + // if your model has specific features, you should probably duplicate this function + ggml_tensor * build_vit( + ggml_tensor * inp, + int64_t n_pos, + norm_type norm_t, + ffn_op_type ffn_t, + ggml_tensor * learned_pos_embd, + std::function add_pos); + + // build the input after conv2d (inp_raw --> patches) + // returns tensor with shape [n_embd, n_patches] + ggml_tensor * build_inp(); + + ggml_tensor * build_inp_raw(int channels = 3); + + ggml_tensor * build_norm( + ggml_tensor * cur, + ggml_tensor * mw, + ggml_tensor * mb, + norm_type type, + float norm_eps, + int il) const; + + ggml_tensor * build_ffn( + ggml_tensor * cur, + ggml_tensor * up, + ggml_tensor * up_b, + ggml_tensor * gate, + ggml_tensor * gate_b, + ggml_tensor * down, + ggml_tensor * down_b, + ffn_op_type type_op, + int il) const; + + ggml_tensor * build_attn( + ggml_tensor * wo, + ggml_tensor * wo_b, + ggml_tensor * q_cur, + ggml_tensor * k_cur, + ggml_tensor * v_cur, + ggml_tensor * kq_mask, + float kq_scale, + int il) const; + + // implementation of the 2D RoPE without adding a new op in ggml + // this is not efficient (use double the memory), but works on all backends + // TODO: there was a more efficient which relies on ggml_view and ggml_rope_ext_inplace, but the rope inplace does not work well with non-contiguous tensors ; we should fix that and revert back to the original implementation in https://github.com/ggml-org/llama.cpp/pull/13065 + ggml_tensor * build_rope_2d( + ggml_context * ctx0, + ggml_tensor * cur, + ggml_tensor * pos_a, // first half + ggml_tensor * pos_b, // second half + const float freq_base, + const bool interleave_freq + ); + + // aka pixel_shuffle / pixel_unshuffle / patch_merger (Kimi-VL) + // support dynamic resolution + ggml_tensor * build_patch_merge_permute(ggml_tensor * cur, int scale_factor); + + // Generic function to stack frames for audio processing + // Abstracts out the StackAudioFrames logic used by ultravox + ggml_tensor * build_stack(ggml_tensor * cur, int32_t stack_factor, int32_t n_embed); +}; diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h index cd47865bf4..9ef0d301e4 100644 --- a/tools/mtmd/clip-impl.h +++ b/tools/mtmd/clip-impl.h @@ -1,3 +1,5 @@ +#pragma once + #include "ggml.h" #include "gguf.h" #include "clip.h" @@ -13,6 +15,8 @@ // Internal header for clip.cpp +#define MTMD_INTERNAL_HEADER + #define KEY_FTYPE "general.file_type" #define KEY_NAME "general.name" #define KEY_DESCRIPTION "general.description" @@ -132,6 +136,10 @@ // align x to upper multiple of n #define CLIP_ALIGN(x, n) ((((x) + (n) - 1) / (n)) * (n)) +// forward declaration +// TODO: improve this later +struct clip_ctx; + enum projector_type { PROJECTOR_TYPE_MLP, PROJECTOR_TYPE_MLP_NORM, @@ -149,6 +157,7 @@ enum projector_type { PROJECTOR_TYPE_INTERNVL, PROJECTOR_TYPE_LLAMA4, PROJECTOR_TYPE_QWEN2A, + PROJECTOR_TYPE_GLMA, PROJECTOR_TYPE_QWEN25O, // will be replaced by QWEN2A or QWEN25VL depending on clip_ctx PROJECTOR_TYPE_VOXTRAL, PROJECTOR_TYPE_LFM2, @@ -175,6 +184,7 @@ static std::map PROJECTOR_TYPE_NAMES = { { PROJECTOR_TYPE_INTERNVL, "internvl"}, { PROJECTOR_TYPE_LLAMA4, "llama4"}, { PROJECTOR_TYPE_QWEN2A, "qwen2a"}, + { PROJECTOR_TYPE_GLMA, "glma"}, { PROJECTOR_TYPE_QWEN25O, "qwen2.5o"}, { PROJECTOR_TYPE_VOXTRAL, "voxtral"}, { PROJECTOR_TYPE_LFM2, "lfm2"}, diff --git a/tools/mtmd/clip-model.h b/tools/mtmd/clip-model.h new file mode 100644 index 0000000000..2f7dbb458e --- /dev/null +++ b/tools/mtmd/clip-model.h @@ -0,0 +1,289 @@ +#pragma once + +#include "ggml.h" +#include "clip.h" +#include "clip-impl.h" + +#include +#include +#include +#include + +enum ffn_op_type { + FFN_GELU, + FFN_GELU_ERF, + FFN_SILU, + FFN_GELU_QUICK, +}; + +enum norm_type { + NORM_TYPE_NORMAL, + NORM_TYPE_RMS, +}; + +enum patch_merge_type { + PATCH_MERGE_FLAT, + PATCH_MERGE_SPATIAL_UNPAD, +}; + +struct clip_hparams { + int32_t image_size = 0; + int32_t patch_size = 0; + int32_t n_embd = 0; + int32_t n_ff = 0; + int32_t projection_dim = 0; + int32_t n_head = 0; + int32_t n_layer = 0; + // idefics3 + int32_t image_longest_edge = 0; + int32_t image_min_pixels = -1; + int32_t image_max_pixels = -1; + int32_t n_merge = 0; // number of patch merges **per-side** + + float image_mean[3]; + float image_std[3]; + + // for models using dynamic image size, we need to have a smaller image size to warmup + // otherwise, user will get OOM everytime they load the model + int32_t warmup_image_size = 0; + int32_t warmup_audio_size = 3000; + + ffn_op_type ffn_op = FFN_GELU; + + patch_merge_type mm_patch_merge_type = PATCH_MERGE_FLAT; + + float eps = 1e-6; + float rope_theta = 0.0; + + std::vector image_res_candidates; // for llava-uhd style models + int32_t image_crop_resolution; + std::unordered_set vision_feature_layer; + int32_t attn_window_size = 0; + int32_t n_wa_pattern = 0; + + // audio + int32_t n_mel_bins = 0; // whisper preprocessor + int32_t proj_stack_factor = 0; // ultravox + + // audio-to-mel preprocessor params + int32_t audio_chunk_len = -1; // in seconds + int32_t audio_sample_rate = -1; + int32_t audio_n_fft = -1; + int32_t audio_window_len = -1; + int32_t audio_hop_len = -1; + + // legacy + bool has_llava_projector = false; + int minicpmv_version = 0; + int32_t minicpmv_query_num = 0; // MiniCPM-V query number + + // custom value provided by user, can be undefined if not set + int32_t custom_image_min_tokens = -1; + int32_t custom_image_max_tokens = -1; + + void set_limit_image_tokens(int n_tokens_min, int n_tokens_max) { + const int cur_merge = n_merge == 0 ? 1 : n_merge; + const int patch_area = patch_size * patch_size * cur_merge * cur_merge; + image_min_pixels = (custom_image_min_tokens > 0 ? custom_image_min_tokens : n_tokens_min) * patch_area; + image_max_pixels = (custom_image_max_tokens > 0 ? custom_image_max_tokens : n_tokens_max) * patch_area; + warmup_image_size = static_cast(std::sqrt(image_max_pixels)); + } + + void set_warmup_n_tokens(int n_tokens) { + int n_tok_per_side = static_cast(std::sqrt(n_tokens)); + GGML_ASSERT(n_tok_per_side * n_tok_per_side == n_tokens && "n_tokens must be n*n"); + const int cur_merge = n_merge == 0 ? 1 : n_merge; + warmup_image_size = n_tok_per_side * patch_size * cur_merge; + // TODO: support warmup size for custom token numbers + } +}; + +struct clip_layer { + // attention + ggml_tensor * k_w = nullptr; + ggml_tensor * k_b = nullptr; + ggml_tensor * q_w = nullptr; + ggml_tensor * q_b = nullptr; + ggml_tensor * v_w = nullptr; + ggml_tensor * v_b = nullptr; + ggml_tensor * qkv_w = nullptr; + ggml_tensor * qkv_b = nullptr; + + ggml_tensor * o_w = nullptr; + ggml_tensor * o_b = nullptr; + + ggml_tensor * k_norm = nullptr; + ggml_tensor * q_norm = nullptr; + + // layernorm 1 + ggml_tensor * ln_1_w = nullptr; + ggml_tensor * ln_1_b = nullptr; + + ggml_tensor * ff_up_w = nullptr; + ggml_tensor * ff_up_b = nullptr; + ggml_tensor * ff_gate_w = nullptr; + ggml_tensor * ff_gate_b = nullptr; + ggml_tensor * ff_down_w = nullptr; + ggml_tensor * ff_down_b = nullptr; + + // layernorm 2 + ggml_tensor * ln_2_w = nullptr; + ggml_tensor * ln_2_b = nullptr; + + // layer scale (no bias) + ggml_tensor * ls_1_w = nullptr; + ggml_tensor * ls_2_w = nullptr; + + // qwen3vl deepstack merger + ggml_tensor * deepstack_norm_w = nullptr; + ggml_tensor * deepstack_norm_b = nullptr; + ggml_tensor * deepstack_fc1_w = nullptr; + ggml_tensor * deepstack_fc1_b = nullptr; + ggml_tensor * deepstack_fc2_w = nullptr; + ggml_tensor * deepstack_fc2_b = nullptr; + + bool has_deepstack() const { + return deepstack_fc1_w != nullptr; + } +}; + +struct clip_model { + clip_modality modality = CLIP_MODALITY_VISION; + projector_type proj_type = PROJECTOR_TYPE_MLP; + clip_hparams hparams; + + // embeddings + ggml_tensor * class_embedding = nullptr; + ggml_tensor * patch_embeddings_0 = nullptr; + ggml_tensor * patch_embeddings_1 = nullptr; // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL) + ggml_tensor * patch_bias = nullptr; + ggml_tensor * position_embeddings = nullptr; + + ggml_tensor * pre_ln_w = nullptr; + ggml_tensor * pre_ln_b = nullptr; + + std::vector layers; + + int32_t n_deepstack_layers = 0; // used by Qwen3-VL, calculated from clip_layer + + ggml_tensor * post_ln_w; + ggml_tensor * post_ln_b; + + ggml_tensor * projection; // TODO: rename it to fc (fully connected layer) + ggml_tensor * mm_fc_w; + ggml_tensor * mm_fc_b; + + // LLaVA projection + ggml_tensor * mm_input_norm_w = nullptr; + ggml_tensor * mm_input_norm_b = nullptr; + ggml_tensor * mm_0_w = nullptr; + ggml_tensor * mm_0_b = nullptr; + ggml_tensor * mm_2_w = nullptr; + ggml_tensor * mm_2_b = nullptr; + + ggml_tensor * image_newline = nullptr; + + // Yi type models with mlp+normalization projection + ggml_tensor * mm_1_w = nullptr; // Yi type models have 0, 1, 3, 4 + ggml_tensor * mm_1_b = nullptr; + ggml_tensor * mm_3_w = nullptr; + ggml_tensor * mm_3_b = nullptr; + ggml_tensor * mm_4_w = nullptr; + ggml_tensor * mm_4_b = nullptr; + + // GLMV-Edge projection + ggml_tensor * mm_model_adapter_conv_w = nullptr; + ggml_tensor * mm_model_adapter_conv_b = nullptr; + + // MobileVLM projection + ggml_tensor * mm_model_mlp_1_w = nullptr; + ggml_tensor * mm_model_mlp_1_b = nullptr; + ggml_tensor * mm_model_mlp_3_w = nullptr; + ggml_tensor * mm_model_mlp_3_b = nullptr; + ggml_tensor * mm_model_block_1_block_0_0_w = nullptr; + ggml_tensor * mm_model_block_1_block_0_1_w = nullptr; + ggml_tensor * mm_model_block_1_block_0_1_b = nullptr; + ggml_tensor * mm_model_block_1_block_1_fc1_w = nullptr; + ggml_tensor * mm_model_block_1_block_1_fc1_b = nullptr; + ggml_tensor * mm_model_block_1_block_1_fc2_w = nullptr; + ggml_tensor * mm_model_block_1_block_1_fc2_b = nullptr; + ggml_tensor * mm_model_block_1_block_2_0_w = nullptr; + ggml_tensor * mm_model_block_1_block_2_1_w = nullptr; + ggml_tensor * mm_model_block_1_block_2_1_b = nullptr; + ggml_tensor * mm_model_block_2_block_0_0_w = nullptr; + ggml_tensor * mm_model_block_2_block_0_1_w = nullptr; + ggml_tensor * mm_model_block_2_block_0_1_b = nullptr; + ggml_tensor * mm_model_block_2_block_1_fc1_w = nullptr; + ggml_tensor * mm_model_block_2_block_1_fc1_b = nullptr; + ggml_tensor * mm_model_block_2_block_1_fc2_w = nullptr; + ggml_tensor * mm_model_block_2_block_1_fc2_b = nullptr; + ggml_tensor * mm_model_block_2_block_2_0_w = nullptr; + ggml_tensor * mm_model_block_2_block_2_1_w = nullptr; + ggml_tensor * mm_model_block_2_block_2_1_b = nullptr; + + // MobileVLM_V2 projection + ggml_tensor * mm_model_mlp_0_w = nullptr; + ggml_tensor * mm_model_mlp_0_b = nullptr; + ggml_tensor * mm_model_mlp_2_w = nullptr; + ggml_tensor * mm_model_mlp_2_b = nullptr; + ggml_tensor * mm_model_peg_0_w = nullptr; + ggml_tensor * mm_model_peg_0_b = nullptr; + + // MINICPMV projection + ggml_tensor * mm_model_pos_embed_k = nullptr; + ggml_tensor * mm_model_query = nullptr; + ggml_tensor * mm_model_proj = nullptr; + ggml_tensor * mm_model_kv_proj = nullptr; + ggml_tensor * mm_model_attn_q_w = nullptr; + ggml_tensor * mm_model_attn_q_b = nullptr; + ggml_tensor * mm_model_attn_k_w = nullptr; + ggml_tensor * mm_model_attn_k_b = nullptr; + ggml_tensor * mm_model_attn_v_w = nullptr; + ggml_tensor * mm_model_attn_v_b = nullptr; + ggml_tensor * mm_model_attn_o_w = nullptr; + ggml_tensor * mm_model_attn_o_b = nullptr; + ggml_tensor * mm_model_ln_q_w = nullptr; + ggml_tensor * mm_model_ln_q_b = nullptr; + ggml_tensor * mm_model_ln_kv_w = nullptr; + ggml_tensor * mm_model_ln_kv_b = nullptr; + ggml_tensor * mm_model_ln_post_w = nullptr; + ggml_tensor * mm_model_ln_post_b = nullptr; + + // gemma3 + ggml_tensor * mm_input_proj_w = nullptr; + ggml_tensor * mm_soft_emb_norm_w = nullptr; + + // pixtral + ggml_tensor * token_embd_img_break = nullptr; + ggml_tensor * mm_patch_merger_w = nullptr; + + // ultravox / whisper encoder + ggml_tensor * conv1d_1_w = nullptr; + ggml_tensor * conv1d_1_b = nullptr; + ggml_tensor * conv1d_2_w = nullptr; + ggml_tensor * conv1d_2_b = nullptr; + ggml_tensor * mm_norm_pre_w = nullptr; + ggml_tensor * mm_norm_pre_b = nullptr; + ggml_tensor * mm_norm_mid_w = nullptr; + + // cogvlm + ggml_tensor * mm_post_fc_norm_w = nullptr; + ggml_tensor * mm_post_fc_norm_b = nullptr; + ggml_tensor * mm_h_to_4h_w = nullptr; + ggml_tensor * mm_gate_w = nullptr; + ggml_tensor * mm_4h_to_h_w = nullptr; + ggml_tensor * mm_boi = nullptr; + ggml_tensor * mm_eoi = nullptr; + + bool audio_has_avgpool() const { + return proj_type == PROJECTOR_TYPE_QWEN2A + || proj_type == PROJECTOR_TYPE_VOXTRAL; + } + + bool audio_has_stack_frames() const { + return proj_type == PROJECTOR_TYPE_ULTRAVOX + || proj_type == PROJECTOR_TYPE_VOXTRAL; + } +}; + +const clip_hparams * clip_get_hparams(const struct clip_ctx * ctx); diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 3ed08a0fec..fee49e465c 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -1,9 +1,9 @@ -// NOTE: This is modified from clip.cpp only for LLaVA, -// so there might be still unnecessary artifacts hanging around -// I'll gradually clean and extend it -// Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch #include "clip.h" #include "clip-impl.h" +#include "clip-model.h" +#include "clip-graph.h" +#include "models/models.h" + #include "ggml.h" #include "ggml-cpp.h" #include "ggml-alloc.h" @@ -26,18 +26,6 @@ struct clip_logger_state g_logger_state = {clip_log_callback_default, NULL}; -enum ffn_op_type { - FFN_GELU, - FFN_GELU_ERF, - FFN_SILU, - FFN_GELU_QUICK, -}; - -enum norm_type { - NORM_TYPE_NORMAL, - NORM_TYPE_RMS, -}; - //#define CLIP_DEBUG_FUNCTIONS #ifdef CLIP_DEBUG_FUNCTIONS @@ -149,267 +137,6 @@ static void clip_image_convert_f32_to_u8(const clip_image_f32& src, clip_image_u #endif -// -// clip layers -// - -enum patch_merge_type { - PATCH_MERGE_FLAT, - PATCH_MERGE_SPATIAL_UNPAD, -}; - -struct clip_hparams { - int32_t image_size = 0; - int32_t patch_size = 0; - int32_t n_embd = 0; - int32_t n_ff = 0; - int32_t projection_dim = 0; - int32_t n_head = 0; - int32_t n_layer = 0; - // idefics3 - int32_t image_longest_edge = 0; - int32_t image_min_pixels = -1; - int32_t image_max_pixels = -1; - int32_t n_merge = 0; // number of patch merges **per-side** - - float image_mean[3]; - float image_std[3]; - - // for models using dynamic image size, we need to have a smaller image size to warmup - // otherwise, user will get OOM everytime they load the model - int32_t warmup_image_size = 0; - int32_t warmup_audio_size = 3000; - - ffn_op_type ffn_op = FFN_GELU; - - patch_merge_type mm_patch_merge_type = PATCH_MERGE_FLAT; - - float eps = 1e-6; - float rope_theta = 0.0; - - std::vector image_res_candidates; // for llava-uhd style models - int32_t image_crop_resolution; - std::unordered_set vision_feature_layer; - int32_t attn_window_size = 0; - int32_t n_wa_pattern = 0; - - // audio - int32_t n_mel_bins = 0; // whisper preprocessor - int32_t proj_stack_factor = 0; // ultravox - - // legacy - bool has_llava_projector = false; - int minicpmv_version = 0; - int32_t minicpmv_query_num = 0; // MiniCPM-V query number - - // custom value provided by user, can be undefined if not set - int32_t custom_image_min_tokens = -1; - int32_t custom_image_max_tokens = -1; - - void set_limit_image_tokens(int n_tokens_min, int n_tokens_max) { - const int cur_merge = n_merge == 0 ? 1 : n_merge; - const int patch_area = patch_size * patch_size * cur_merge * cur_merge; - image_min_pixels = (custom_image_min_tokens > 0 ? custom_image_min_tokens : n_tokens_min) * patch_area; - image_max_pixels = (custom_image_max_tokens > 0 ? custom_image_max_tokens : n_tokens_max) * patch_area; - warmup_image_size = static_cast(std::sqrt(image_max_pixels)); - } - - void set_warmup_n_tokens(int n_tokens) { - int n_tok_per_side = static_cast(std::sqrt(n_tokens)); - GGML_ASSERT(n_tok_per_side * n_tok_per_side == n_tokens && "n_tokens must be n*n"); - const int cur_merge = n_merge == 0 ? 1 : n_merge; - warmup_image_size = n_tok_per_side * patch_size * cur_merge; - // TODO: support warmup size for custom token numbers - } -}; - -struct clip_layer { - // attention - ggml_tensor * k_w = nullptr; - ggml_tensor * k_b = nullptr; - ggml_tensor * q_w = nullptr; - ggml_tensor * q_b = nullptr; - ggml_tensor * v_w = nullptr; - ggml_tensor * v_b = nullptr; - ggml_tensor * qkv_w = nullptr; - ggml_tensor * qkv_b = nullptr; - - ggml_tensor * o_w = nullptr; - ggml_tensor * o_b = nullptr; - - ggml_tensor * k_norm = nullptr; - ggml_tensor * q_norm = nullptr; - - // layernorm 1 - ggml_tensor * ln_1_w = nullptr; - ggml_tensor * ln_1_b = nullptr; - - ggml_tensor * ff_up_w = nullptr; - ggml_tensor * ff_up_b = nullptr; - ggml_tensor * ff_gate_w = nullptr; - ggml_tensor * ff_gate_b = nullptr; - ggml_tensor * ff_down_w = nullptr; - ggml_tensor * ff_down_b = nullptr; - - // layernorm 2 - ggml_tensor * ln_2_w = nullptr; - ggml_tensor * ln_2_b = nullptr; - - // layer scale (no bias) - ggml_tensor * ls_1_w = nullptr; - ggml_tensor * ls_2_w = nullptr; - - // qwen3vl deepstack merger - ggml_tensor * deepstack_norm_w = nullptr; - ggml_tensor * deepstack_norm_b = nullptr; - ggml_tensor * deepstack_fc1_w = nullptr; - ggml_tensor * deepstack_fc1_b = nullptr; - ggml_tensor * deepstack_fc2_w = nullptr; - ggml_tensor * deepstack_fc2_b = nullptr; - - bool has_deepstack() const { - return deepstack_fc1_w != nullptr; - } -}; - -struct clip_model { - clip_modality modality = CLIP_MODALITY_VISION; - projector_type proj_type = PROJECTOR_TYPE_MLP; - clip_hparams hparams; - - // embeddings - ggml_tensor * class_embedding = nullptr; - ggml_tensor * patch_embeddings_0 = nullptr; - ggml_tensor * patch_embeddings_1 = nullptr; // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL) - ggml_tensor * patch_bias = nullptr; - ggml_tensor * position_embeddings = nullptr; - - ggml_tensor * pre_ln_w = nullptr; - ggml_tensor * pre_ln_b = nullptr; - - std::vector layers; - - int32_t n_deepstack_layers = 0; // used by Qwen3-VL, calculated from clip_layer - - ggml_tensor * post_ln_w; - ggml_tensor * post_ln_b; - - ggml_tensor * projection; // TODO: rename it to fc (fully connected layer) - ggml_tensor * mm_fc_w; - ggml_tensor * mm_fc_b; - - // LLaVA projection - ggml_tensor * mm_input_norm_w = nullptr; - ggml_tensor * mm_input_norm_b = nullptr; - ggml_tensor * mm_0_w = nullptr; - ggml_tensor * mm_0_b = nullptr; - ggml_tensor * mm_2_w = nullptr; - ggml_tensor * mm_2_b = nullptr; - - ggml_tensor * image_newline = nullptr; - - // Yi type models with mlp+normalization projection - ggml_tensor * mm_1_w = nullptr; // Yi type models have 0, 1, 3, 4 - ggml_tensor * mm_1_b = nullptr; - ggml_tensor * mm_3_w = nullptr; - ggml_tensor * mm_3_b = nullptr; - ggml_tensor * mm_4_w = nullptr; - ggml_tensor * mm_4_b = nullptr; - - // GLMV-Edge projection - ggml_tensor * mm_model_adapter_conv_w = nullptr; - ggml_tensor * mm_model_adapter_conv_b = nullptr; - - // MobileVLM projection - ggml_tensor * mm_model_mlp_1_w = nullptr; - ggml_tensor * mm_model_mlp_1_b = nullptr; - ggml_tensor * mm_model_mlp_3_w = nullptr; - ggml_tensor * mm_model_mlp_3_b = nullptr; - ggml_tensor * mm_model_block_1_block_0_0_w = nullptr; - ggml_tensor * mm_model_block_1_block_0_1_w = nullptr; - ggml_tensor * mm_model_block_1_block_0_1_b = nullptr; - ggml_tensor * mm_model_block_1_block_1_fc1_w = nullptr; - ggml_tensor * mm_model_block_1_block_1_fc1_b = nullptr; - ggml_tensor * mm_model_block_1_block_1_fc2_w = nullptr; - ggml_tensor * mm_model_block_1_block_1_fc2_b = nullptr; - ggml_tensor * mm_model_block_1_block_2_0_w = nullptr; - ggml_tensor * mm_model_block_1_block_2_1_w = nullptr; - ggml_tensor * mm_model_block_1_block_2_1_b = nullptr; - ggml_tensor * mm_model_block_2_block_0_0_w = nullptr; - ggml_tensor * mm_model_block_2_block_0_1_w = nullptr; - ggml_tensor * mm_model_block_2_block_0_1_b = nullptr; - ggml_tensor * mm_model_block_2_block_1_fc1_w = nullptr; - ggml_tensor * mm_model_block_2_block_1_fc1_b = nullptr; - ggml_tensor * mm_model_block_2_block_1_fc2_w = nullptr; - ggml_tensor * mm_model_block_2_block_1_fc2_b = nullptr; - ggml_tensor * mm_model_block_2_block_2_0_w = nullptr; - ggml_tensor * mm_model_block_2_block_2_1_w = nullptr; - ggml_tensor * mm_model_block_2_block_2_1_b = nullptr; - - // MobileVLM_V2 projection - ggml_tensor * mm_model_mlp_0_w = nullptr; - ggml_tensor * mm_model_mlp_0_b = nullptr; - ggml_tensor * mm_model_mlp_2_w = nullptr; - ggml_tensor * mm_model_mlp_2_b = nullptr; - ggml_tensor * mm_model_peg_0_w = nullptr; - ggml_tensor * mm_model_peg_0_b = nullptr; - - // MINICPMV projection - ggml_tensor * mm_model_pos_embed_k = nullptr; - ggml_tensor * mm_model_query = nullptr; - ggml_tensor * mm_model_proj = nullptr; - ggml_tensor * mm_model_kv_proj = nullptr; - ggml_tensor * mm_model_attn_q_w = nullptr; - ggml_tensor * mm_model_attn_q_b = nullptr; - ggml_tensor * mm_model_attn_k_w = nullptr; - ggml_tensor * mm_model_attn_k_b = nullptr; - ggml_tensor * mm_model_attn_v_w = nullptr; - ggml_tensor * mm_model_attn_v_b = nullptr; - ggml_tensor * mm_model_attn_o_w = nullptr; - ggml_tensor * mm_model_attn_o_b = nullptr; - ggml_tensor * mm_model_ln_q_w = nullptr; - ggml_tensor * mm_model_ln_q_b = nullptr; - ggml_tensor * mm_model_ln_kv_w = nullptr; - ggml_tensor * mm_model_ln_kv_b = nullptr; - ggml_tensor * mm_model_ln_post_w = nullptr; - ggml_tensor * mm_model_ln_post_b = nullptr; - - // gemma3 - ggml_tensor * mm_input_proj_w = nullptr; - ggml_tensor * mm_soft_emb_norm_w = nullptr; - - // pixtral - ggml_tensor * token_embd_img_break = nullptr; - ggml_tensor * mm_patch_merger_w = nullptr; - - // ultravox / whisper encoder - ggml_tensor * conv1d_1_w = nullptr; - ggml_tensor * conv1d_1_b = nullptr; - ggml_tensor * conv1d_2_w = nullptr; - ggml_tensor * conv1d_2_b = nullptr; - ggml_tensor * mm_norm_pre_w = nullptr; - ggml_tensor * mm_norm_mid_w = nullptr; - - // cogvlm - ggml_tensor * mm_post_fc_norm_w = nullptr; - ggml_tensor * mm_post_fc_norm_b = nullptr; - ggml_tensor * mm_h_to_4h_w = nullptr; - ggml_tensor * mm_gate_w = nullptr; - ggml_tensor * mm_4h_to_h_w = nullptr; - ggml_tensor * mm_boi = nullptr; - ggml_tensor * mm_eoi = nullptr; - - bool audio_has_avgpool() const { - return proj_type == PROJECTOR_TYPE_QWEN2A - || proj_type == PROJECTOR_TYPE_VOXTRAL; - } - - bool audio_has_stack_frames() const { - return proj_type == PROJECTOR_TYPE_ULTRAVOX - || proj_type == PROJECTOR_TYPE_VOXTRAL; - } -}; - struct clip_ctx { clip_model model; @@ -492,1599 +219,150 @@ struct clip_ctx { } }; -struct clip_graph { - clip_ctx * ctx; - const clip_model & model; - const clip_hparams & hparams; +// +// clip_graph +// - // we only support single image per batch - const clip_image_f32 & img; +clip_graph::clip_graph(clip_ctx * ctx, const clip_image_f32 & img) : + model(ctx->model), + hparams(model.hparams), + proj_type(ctx->proj_type()), + img(img), + patch_size(hparams.patch_size), + n_patches_x(img.nx / patch_size), + n_patches_y(img.ny / patch_size), + n_patches(n_patches_x * n_patches_y), + n_embd(hparams.n_embd), + n_head(hparams.n_head), + d_head(n_embd / n_head), + n_layer(hparams.n_layer), + n_mmproj_embd(clip_n_mmproj_embd(ctx)), + eps(hparams.eps), + kq_scale(1.0f / sqrtf((float)d_head)), + flash_attn_type(ctx->flash_attn_type), + debug_graph(ctx->debug_graph), + debug_print_tensors(ctx->debug_print_tensors) { + struct ggml_init_params params = { + /*.mem_size =*/ ctx->buf_compute_meta.size(), + /*.mem_buffer =*/ ctx->buf_compute_meta.data(), + /*.no_alloc =*/ true, + }; + ctx0_ptr.reset(ggml_init(params)); + ctx0 = ctx0_ptr.get(); + gf = ggml_new_graph_custom(ctx0, ctx->max_nodes, false); +} - const int patch_size; - const int n_patches_x; - const int n_patches_y; - const int n_patches; - const int n_embd; - const int n_head; - const int d_head; - const int n_layer; - const float eps; - const float kq_scale; - - ggml_context_ptr ctx0_ptr; - ggml_context * ctx0; - ggml_cgraph * gf; - - clip_graph(clip_ctx * ctx, const clip_image_f32 & img) : - ctx(ctx), - model(ctx->model), - hparams(model.hparams), - img(img), - patch_size(hparams.patch_size), - n_patches_x(img.nx / patch_size), - n_patches_y(img.ny / patch_size), - n_patches(n_patches_x * n_patches_y), - n_embd(hparams.n_embd), - n_head(hparams.n_head), - d_head(n_embd / n_head), - n_layer(hparams.n_layer), - eps(hparams.eps), - kq_scale(1.0f / sqrtf((float)d_head)) { - struct ggml_init_params params = { - /*.mem_size =*/ ctx->buf_compute_meta.size(), - /*.mem_buffer =*/ ctx->buf_compute_meta.data(), - /*.no_alloc =*/ true, - }; - ctx0_ptr.reset(ggml_init(params)); - ctx0 = ctx0_ptr.get(); - gf = ggml_new_graph_custom(ctx0, ctx->max_nodes, false); - } - - ggml_cgraph * build_siglip() { - ggml_tensor * inp = build_inp(); - - ggml_tensor * learned_pos_embd = model.position_embeddings; - if (ctx->proj_type() == PROJECTOR_TYPE_LFM2) { - learned_pos_embd = resize_position_embeddings(); - } - - ggml_tensor * cur = build_vit( - inp, n_patches, - NORM_TYPE_NORMAL, - hparams.ffn_op, - learned_pos_embd, - nullptr); - - if (ctx->proj_type() == PROJECTOR_TYPE_GEMMA3) { - const int batch_size = 1; - GGML_ASSERT(n_patches_x == n_patches_y); - const int patches_per_image = n_patches_x; - const int kernel_size = hparams.n_merge; - - cur = ggml_transpose(ctx0, cur); - cur = ggml_cont_4d(ctx0, cur, patches_per_image, patches_per_image, n_embd, batch_size); - - // doing a pool2d to reduce the number of output tokens - cur = ggml_pool_2d(ctx0, cur, GGML_OP_POOL_AVG, kernel_size, kernel_size, kernel_size, kernel_size, 0, 0); - cur = ggml_reshape_3d(ctx0, cur, cur->ne[0] * cur->ne[0], n_embd, batch_size); - cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur)); - - // apply norm before projection - cur = ggml_rms_norm(ctx0, cur, eps); - cur = ggml_mul(ctx0, cur, model.mm_soft_emb_norm_w); - - // apply projection - cur = ggml_mul_mat(ctx0, - ggml_cont(ctx0, ggml_transpose(ctx0, model.mm_input_proj_w)), - cur); - - } else if (ctx->proj_type() == PROJECTOR_TYPE_IDEFICS3) { - // pixel_shuffle - // https://github.com/huggingface/transformers/blob/0a950e0bbe1ed58d5401a6b547af19f15f0c195e/src/transformers/models/idefics3/modeling_idefics3.py#L578 - const int scale_factor = model.hparams.n_merge; - cur = build_patch_merge_permute(cur, scale_factor); - cur = ggml_mul_mat(ctx0, model.projection, cur); - - } else if (ctx->proj_type() == PROJECTOR_TYPE_LFM2) { - // pixel unshuffle block - const int scale_factor = model.hparams.n_merge; - cur = build_patch_merge_permute(cur, scale_factor); - - // projection - cur = ggml_norm(ctx0, cur, 1e-5); // default nn.LayerNorm - cur = ggml_mul(ctx0, cur, model.mm_input_norm_w); - cur = ggml_add(ctx0, cur, model.mm_input_norm_b); - - cur = ggml_mul_mat(ctx0, model.mm_1_w, cur); - cur = ggml_add(ctx0, cur, model.mm_1_b); - cur = ggml_gelu(ctx0, cur); - cur = ggml_mul_mat(ctx0, model.mm_2_w, cur); - cur = ggml_add(ctx0, cur, model.mm_2_b); - - } else if (ctx->proj_type() == PROJECTOR_TYPE_JANUS_PRO) { - cur = build_ffn(cur, - model.mm_0_w, model.mm_0_b, - nullptr, nullptr, - model.mm_1_w, model.mm_1_b, - hparams.ffn_op, - -1); - - } else { - GGML_ABORT("SigLIP: Unsupported projector type"); - } - - // build the graph +void clip_graph::cb(ggml_tensor * cur0, const char * name, int il) const { + if (debug_graph) { + ggml_tensor * cur = ggml_cpy(ctx0, cur0, ggml_dup_tensor(ctx0, cur0)); + std::string cur_name = il >= 0 ? std::string(name) + "_" + std::to_string(il) : name; + ggml_set_name(cur, cur_name.c_str()); + ggml_set_output(cur); ggml_build_forward_expand(gf, cur); - - return gf; + debug_print_tensors.push_back(cur); } +} - ggml_cgraph * build_pixtral() { - const int n_merge = hparams.n_merge; +// siglip2 naflex +ggml_tensor * clip_graph::resize_position_embeddings() { + ggml_tensor * pos_embd = model.position_embeddings; + const int height = img.ny / patch_size; + const int width = img.nx / patch_size; + const uint32_t mode = GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ANTIALIAS; + const int n_per_side = (int)std::sqrt(pos_embd->ne[1]); - // 2D input positions - ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); - ggml_set_name(pos_h, "pos_h"); - ggml_set_input(pos_h); - - ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); - ggml_set_name(pos_w, "pos_w"); - ggml_set_input(pos_w); - - auto add_pos = [&](ggml_tensor * cur, const clip_layer &) { - return build_rope_2d(ctx0, cur, pos_h, pos_w, hparams.rope_theta, true); - }; - - ggml_tensor * inp = build_inp(); - ggml_tensor * cur = build_vit( - inp, n_patches, - NORM_TYPE_RMS, - hparams.ffn_op, - nullptr, // no learned pos embd - add_pos); - - // mistral small 3.1 patch merger - // ref: https://github.com/huggingface/transformers/blob/7a3e208892c06a5e278144eaf38c8599a42f53e7/src/transformers/models/mistral3/modeling_mistral3.py#L67 - if (model.mm_patch_merger_w) { - GGML_ASSERT(hparams.n_merge > 0); - - cur = ggml_mul(ctx0, ggml_rms_norm(ctx0, cur, eps), model.mm_input_norm_w); - - // reshape image tokens to 2D grid - cur = ggml_reshape_3d(ctx0, cur, n_embd, n_patches_x, n_patches_y); - cur = ggml_permute(ctx0, cur, 2, 0, 1, 3); // [x, y, n_embd] - cur = ggml_cont(ctx0, cur); - - // torch.nn.functional.unfold is just an im2col under the hood - // we just need a dummy kernel to make it work - ggml_tensor * kernel = ggml_view_3d(ctx0, cur, n_merge, n_merge, cur->ne[2], 0, 0, 0); - cur = ggml_im2col(ctx0, kernel, cur, n_merge, n_merge, 0, 0, 1, 1, true, inp->type); - - // project to n_embd - cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]); - cur = ggml_mul_mat(ctx0, model.mm_patch_merger_w, cur); - } - - // LlavaMultiModalProjector (always using GELU activation) - { - cur = ggml_mul_mat(ctx0, model.mm_1_w, cur); - if (model.mm_1_b) { - cur = ggml_add(ctx0, cur, model.mm_1_b); - } - - cur = ggml_gelu(ctx0, cur); - cur = ggml_mul_mat(ctx0, model.mm_2_w, cur); - if (model.mm_2_b) { - cur = ggml_add(ctx0, cur, model.mm_2_b); - } - } - - // arrangement of the [IMG_BREAK] token - if (model.token_embd_img_break) { - // not efficient, but works - // the trick is to view the embeddings as a 3D tensor with shape [n_embd, n_patches_per_row, n_rows] - // and then concatenate the [IMG_BREAK] token to the end of each row, aka n_patches_per_row dimension - // after the concatenation, we have a tensor with shape [n_embd, n_patches_per_row + 1, n_rows] - - const int p_y = n_merge > 0 ? n_patches_y / n_merge : n_patches_y; - const int p_x = n_merge > 0 ? n_patches_x / n_merge : n_patches_x; - const int p_total = p_x * p_y; - const int n_embd_text = cur->ne[0]; - const int n_tokens_output = p_total + p_y - 1; // one [IMG_BREAK] per row, except the last row - - ggml_tensor * tmp = ggml_reshape_3d(ctx0, cur, n_embd_text, p_x, p_y); - ggml_tensor * tok = ggml_new_tensor_3d(ctx0, tmp->type, n_embd_text, 1, p_y); - tok = ggml_scale(ctx0, tok, 0.0); // clear the tensor - tok = ggml_add(ctx0, tok, model.token_embd_img_break); - tmp = ggml_concat(ctx0, tmp, tok, 1); - cur = ggml_view_2d(ctx0, tmp, - n_embd_text, n_tokens_output, - ggml_row_size(tmp->type, n_embd_text), 0); - } - - // build the graph - ggml_build_forward_expand(gf, cur); - - return gf; - } - - // Qwen2VL and Qwen2.5VL use M-RoPE - ggml_cgraph * build_qwen2vl() { - GGML_ASSERT(model.patch_bias == nullptr); - GGML_ASSERT(model.class_embedding == nullptr); - - const int batch_size = 1; - const bool use_window_attn = hparams.n_wa_pattern > 0; - const int n_wa_pattern = hparams.n_wa_pattern; - const int n_pos = n_patches; - const int num_position_ids = n_pos * 4; // m-rope requires 4 dim per position - - norm_type norm_t = ctx->proj_type() == PROJECTOR_TYPE_QWEN25VL - ? NORM_TYPE_RMS // qwen 2.5 vl - : NORM_TYPE_NORMAL; // qwen 2 vl - - int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4}; - - ggml_tensor * inp_raw = build_inp_raw(); - ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - - GGML_ASSERT(img.nx % (patch_size * 2) == 0); - GGML_ASSERT(img.ny % (patch_size * 2) == 0); - - // second conv dimension - { - auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - inp = ggml_add(ctx0, inp, inp_1); - - inp = ggml_permute(ctx0, inp, 1, 2, 0, 3); // [w, h, c, b] -> [c, w, h, b] - inp = ggml_cont_4d( - ctx0, inp, - n_embd * 2, n_patches_x / 2, n_patches_y, batch_size); - inp = ggml_reshape_4d( - ctx0, inp, - n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2)); - inp = ggml_permute(ctx0, inp, 0, 2, 1, 3); - inp = ggml_cont_3d( - ctx0, inp, - n_embd, n_patches_x * n_patches_y, batch_size); - } - - ggml_tensor * inpL = inp; - ggml_tensor * window_mask = nullptr; - ggml_tensor * window_idx = nullptr; - ggml_tensor * inv_window_idx = nullptr; - - ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids); - ggml_set_name(positions, "positions"); - ggml_set_input(positions); - - // pre-layernorm - if (model.pre_ln_w) { - inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1); - } - - if (use_window_attn) { - // handle window attention inputs - inv_window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / 4); - ggml_set_name(inv_window_idx, "inv_window_idx"); - ggml_set_input(inv_window_idx); - // mask for window attention - window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos); - ggml_set_name(window_mask, "window_mask"); - ggml_set_input(window_mask); - - // if flash attn is used, we need to pad the mask and cast to f16 - if (ctx->flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) { - int n_pad = GGML_PAD(window_mask->ne[1], GGML_KQ_MASK_PAD) - window_mask->ne[1]; - if (n_pad > 0) { - window_mask = ggml_pad(ctx0, window_mask, 0, n_pad, 0, 0); - } - window_mask = ggml_cast(ctx0, window_mask, GGML_TYPE_F16); - } - - // inpL shape: [n_embd, n_patches_x * n_patches_y, batch_size] - GGML_ASSERT(batch_size == 1); - inpL = ggml_reshape_2d(ctx0, inpL, n_embd * 4, n_patches_x * n_patches_y * batch_size / 4); - inpL = ggml_get_rows(ctx0, inpL, inv_window_idx); - inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_patches_x * n_patches_y, batch_size); - } - - // loop over layers - for (int il = 0; il < n_layer; il++) { - auto & layer = model.layers[il]; - const bool full_attn = use_window_attn ? (il + 1) % n_wa_pattern == 0 : true; - - ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states - - // layernorm1 - cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il); - cb(cur, "ln1", il); - - // self-attention - { - ggml_tensor * Qcur = ggml_add(ctx0, - ggml_mul_mat(ctx0, layer.q_w, cur), layer.q_b); - ggml_tensor * Kcur = ggml_add(ctx0, - ggml_mul_mat(ctx0, layer.k_w, cur), layer.k_b); - ggml_tensor * Vcur = ggml_add(ctx0, - ggml_mul_mat(ctx0, layer.v_w, cur), layer.v_b); - - Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_patches); - Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_patches); - Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_patches); - - cb(Qcur, "Qcur", il); - cb(Kcur, "Kcur", il); - cb(Vcur, "Vcur", il); - - // apply M-RoPE - Qcur = ggml_rope_multi( - ctx0, Qcur, positions, nullptr, - d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); - Kcur = ggml_rope_multi( - ctx0, Kcur, positions, nullptr, - d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); - - cb(Qcur, "Qcur_rope", il); - cb(Kcur, "Kcur_rope", il); - - ggml_tensor * attn_mask = full_attn ? nullptr : window_mask; - - cur = build_attn(layer.o_w, layer.o_b, - Qcur, Kcur, Vcur, attn_mask, kq_scale, il); - cb(cur, "attn_out", il); - } - - // re-add the layer input, e.g., residual - cur = ggml_add(ctx0, cur, inpL); - - inpL = cur; // inpL = residual, cur = hidden_states - - cb(cur, "ffn_inp", il); - - // layernorm2 - cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il); - cb(cur, "ffn_inp_normed", il); - - // ffn - cur = build_ffn(cur, - layer.ff_up_w, layer.ff_up_b, - layer.ff_gate_w, layer.ff_gate_b, - layer.ff_down_w, layer.ff_down_b, - hparams.ffn_op, il); - - cb(cur, "ffn_out", il); - - // residual 2 - cur = ggml_add(ctx0, inpL, cur); - cb(cur, "layer_out", il); - - inpL = cur; - } - - // post-layernorm - if (model.post_ln_w) { - inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, n_layer); - } - - // multimodal projection - ggml_tensor * embeddings = inpL; - embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, n_pos / 4, batch_size); - - embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); - - // GELU activation - embeddings = ggml_gelu(ctx0, embeddings); - - // Second linear layer - embeddings = ggml_mul_mat(ctx0, model.mm_1_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_1_b); - - if (use_window_attn) { - window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / 4); - ggml_set_name(window_idx, "window_idx"); - ggml_set_input(window_idx); - - // embeddings shape: [n_embd, n_patches_x * n_patches_y, batch_size] - GGML_ASSERT(batch_size == 1); - embeddings = ggml_reshape_2d(ctx0, embeddings, hparams.projection_dim, n_patches_x * n_patches_y / 4); - embeddings = ggml_get_rows(ctx0, embeddings, window_idx); - embeddings = ggml_reshape_3d(ctx0, embeddings, hparams.projection_dim, n_patches_x * n_patches_y / 4, batch_size); - } - - // build the graph - ggml_build_forward_expand(gf, embeddings); - - return gf; - } - - // Qwen3VL - ggml_cgraph * build_qwen3vl() { - GGML_ASSERT(model.patch_bias != nullptr); - GGML_ASSERT(model.position_embeddings != nullptr); - GGML_ASSERT(model.class_embedding == nullptr); - - const int batch_size = 1; - const int n_pos = n_patches; - const int num_position_ids = n_pos * 4; // m-rope requires 4 dim per position - - norm_type norm_t = NORM_TYPE_NORMAL; - - int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4}; - - ggml_tensor * inp_raw = build_inp_raw(); - ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - - GGML_ASSERT(img.nx % (patch_size * 2) == 0); - GGML_ASSERT(img.ny % (patch_size * 2) == 0); - - // second conv dimension - { - auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - inp = ggml_add(ctx0, inp, inp_1); - - inp = ggml_permute(ctx0, inp, 1, 2, 0, 3); // [w, h, c, b] -> [c, w, h, b] - inp = ggml_cont_4d( - ctx0, inp, - n_embd * 2, n_patches_x / 2, n_patches_y, batch_size); - inp = ggml_reshape_4d( - ctx0, inp, - n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2)); - inp = ggml_permute(ctx0, inp, 0, 2, 1, 3); - inp = ggml_cont_3d( - ctx0, inp, - n_embd, n_patches_x * n_patches_y, batch_size); - } - - // add patch bias - if (model.patch_bias != nullptr) { - inp = ggml_add(ctx0, inp, model.patch_bias); - cb(inp, "patch_bias", -1); - } - - // calculate absolute position embedding and apply - ggml_tensor * learned_pos_embd = resize_position_embeddings(); - learned_pos_embd = ggml_cont_4d( - ctx0, learned_pos_embd, - n_embd * 2, n_patches_x / 2, n_patches_y, batch_size); - learned_pos_embd = ggml_reshape_4d( - ctx0, learned_pos_embd, - n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2)); - learned_pos_embd = ggml_permute(ctx0, learned_pos_embd, 0, 2, 1, 3); - learned_pos_embd = ggml_cont_3d( - ctx0, learned_pos_embd, - n_embd, n_patches_x * n_patches_y, batch_size); - inp = ggml_add(ctx0, inp, learned_pos_embd); - cb(inp, "inp_pos_emb", -1); - - ggml_tensor * inpL = inp; - - ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids); - ggml_set_name(positions, "positions"); - ggml_set_input(positions); - - // pre-layernorm - if (model.pre_ln_w) { - inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1); - } - - // deepstack features (stack along the feature dimension), [n_embd * len(deepstack_layers), n_patches_x * n_patches_y, batch_size] - ggml_tensor * deepstack_features = nullptr; - const int merge_factor = hparams.n_merge > 0 ? hparams.n_merge * hparams.n_merge : 4; // default 2x2=4 for qwen3vl - - // loop over layers - for (int il = 0; il < n_layer; il++) { - auto & layer = model.layers[il]; - - ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states - - // layernorm1 - cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il); - cb(cur, "ln1", il); - - // self-attention - { - cur = ggml_mul_mat(ctx0, layer.qkv_w, cur); - cur = ggml_add(ctx0, cur, layer.qkv_b); - - ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, - /* nb1 */ ggml_row_size(cur->type, d_head), - /* nb2 */ cur->nb[1], - /* offset */ 0); - - ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, - /* nb1 */ ggml_row_size(cur->type, d_head), - /* nb2 */ cur->nb[1], - /* offset */ ggml_row_size(cur->type, n_embd)); - - ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, - /* nb1 */ ggml_row_size(cur->type, d_head), - /* nb2 */ cur->nb[1], - /* offset */ ggml_row_size(cur->type, 2 * n_embd)); - - cb(Qcur, "Qcur", il); - cb(Kcur, "Kcur", il); - cb(Vcur, "Vcur", il); - - // apply M-RoPE - Qcur = ggml_rope_multi( - ctx0, Qcur, positions, nullptr, - d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); - Kcur = ggml_rope_multi( - ctx0, Kcur, positions, nullptr, - d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); - - cb(Qcur, "Qcur_rope", il); - cb(Kcur, "Kcur_rope", il); - - cur = build_attn(layer.o_w, layer.o_b, - Qcur, Kcur, Vcur, nullptr, kq_scale, il); - cb(cur, "attn_out", il); - } - - // re-add the layer input, e.g., residual - cur = ggml_add(ctx0, cur, inpL); - - inpL = cur; // inpL = residual, cur = hidden_states - - cb(cur, "ffn_inp", il); - - // layernorm2 - cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il); - cb(cur, "ffn_inp_normed", il); - - // ffn - cur = build_ffn(cur, - layer.ff_up_w, layer.ff_up_b, - layer.ff_gate_w, layer.ff_gate_b, - layer.ff_down_w, layer.ff_down_b, - hparams.ffn_op, il); - - cb(cur, "ffn_out", il); - - // residual 2 - cur = ggml_add(ctx0, inpL, cur); - cb(cur, "layer_out", il); - - if (layer.has_deepstack()) { - ggml_tensor * feat = ggml_reshape_3d(ctx0, cur, n_embd * merge_factor, n_pos / merge_factor, batch_size); - feat = build_norm(feat, layer.deepstack_norm_w, layer.deepstack_norm_b, norm_t, eps, il); - feat = build_ffn(feat, - layer.deepstack_fc1_w, layer.deepstack_fc1_b, - nullptr, nullptr, - layer.deepstack_fc2_w, layer.deepstack_fc2_b, - ffn_op_type::FFN_GELU, il); - - if(!deepstack_features) { - deepstack_features = feat; - } else { - // concat along the feature dimension - deepstack_features = ggml_concat(ctx0, deepstack_features, feat, 0); - } - } - - inpL = cur; - } - - // post-layernorm - if (model.post_ln_w) { - inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, n_layer); - } - - // multimodal projection - ggml_tensor * embeddings = inpL; - embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, n_pos / 4, batch_size); - - embeddings = build_ffn(embeddings, - model.mm_0_w, model.mm_0_b, - nullptr, nullptr, - model.mm_1_w, model.mm_1_b, - ffn_op_type::FFN_GELU, -1); - - embeddings = ggml_concat(ctx0, embeddings, deepstack_features, 0); // concat along the feature dimension - - // build the graph - ggml_build_forward_expand(gf, embeddings); - - return gf; - } - - ggml_cgraph * build_minicpmv() { - GGML_ASSERT(model.class_embedding == nullptr); - const int n_pos = n_patches; - const int n_embd_proj = clip_n_mmproj_embd(ctx); - - // position embeddings for the projector (not for ViT) - // see: https://huggingface.co/openbmb/MiniCPM-o-2_6/blob/main/resampler.py#L70 - // base frequency omega - ggml_tensor * omega = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_embd_proj / 4); - ggml_set_name(omega, "omega"); - ggml_set_input(omega); - - // 2D input positions (using float for sinusoidal embeddings) - ggml_tensor * pos_h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_pos); - ggml_set_name(pos_h, "pos_h"); - ggml_set_input(pos_h); - ggml_tensor * pos_w = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_pos); - ggml_set_name(pos_w, "pos_w"); - ggml_set_input(pos_w); - - // for selecting learned pos embd, used by ViT - struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); - ggml_set_name(positions, "positions"); - ggml_set_input(positions); - - ggml_tensor * learned_pos_embd = ggml_get_rows(ctx0, model.position_embeddings, positions); - - ggml_tensor * inp = build_inp(); - ggml_tensor * embeddings = build_vit( - inp, n_pos, - NORM_TYPE_NORMAL, - hparams.ffn_op, - learned_pos_embd, - nullptr); - - // resampler projector (it is just another transformer) - - ggml_tensor * q = model.mm_model_query; - ggml_tensor * v = ggml_mul_mat(ctx0, model.mm_model_kv_proj, embeddings); - - // norm - q = build_norm(q, model.mm_model_ln_q_w, model.mm_model_ln_q_b, NORM_TYPE_NORMAL, eps, -1); - v = build_norm(v, model.mm_model_ln_kv_w, model.mm_model_ln_kv_b, NORM_TYPE_NORMAL, eps, -1); - - // calculate sinusoidal pos embd - ggml_tensor * pos_embed = nullptr; - { - // outer product - ggml_tensor * omega_b = ggml_repeat_4d(ctx0, omega, omega->ne[0], n_pos, 1, 1); // n_pos rows - ggml_tensor * theta_x = ggml_mul(ctx0, omega_b, pos_w); - ggml_tensor * theta_y = ggml_mul(ctx0, omega_b, pos_h); - // sin and cos - ggml_tensor * pos_embd_x = ggml_concat( - ctx0, - ggml_sin(ctx0, theta_x), - ggml_cos(ctx0, theta_x), - 0 // concat on first dim - ); - ggml_tensor * pos_embd_y = ggml_concat( - ctx0, - ggml_sin(ctx0, theta_y), - ggml_cos(ctx0, theta_y), - 0 // concat on first dim - ); - pos_embed = ggml_concat(ctx0, pos_embd_x, pos_embd_y, 0); - } - - // k = v + pos_embed - ggml_tensor * k = ggml_add(ctx0, v, pos_embed); - - // attention - { - const int d_head = 128; - int n_head = n_embd_proj/d_head; - // Use actual config value if available, otherwise fall back to hardcoded values - int num_query = ctx->model.hparams.minicpmv_query_num; - ggml_tensor * Q = ggml_add(ctx0, - ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q), - model.mm_model_attn_q_b); - ggml_tensor * K = ggml_add(ctx0, - ggml_mul_mat(ctx0, model.mm_model_attn_k_w, k), - model.mm_model_attn_k_b); - ggml_tensor * V = ggml_add(ctx0, - ggml_mul_mat(ctx0, model.mm_model_attn_v_w, v), - model.mm_model_attn_v_b); - - Q = ggml_reshape_3d(ctx0, Q, d_head, n_head, num_query); - K = ggml_reshape_3d(ctx0, K, d_head, n_head, n_pos); - V = ggml_reshape_3d(ctx0, V, d_head, n_head, n_pos); - - cb(Q, "resampler_Q", -1); - cb(K, "resampler_K", -1); - cb(V, "resampler_V", -1); - - float resampler_kq_scale = 1.0f/ sqrtf(float(d_head)); - embeddings = build_attn( - model.mm_model_attn_o_w, - model.mm_model_attn_o_b, - Q, K, V, nullptr, resampler_kq_scale, -1); - cb(embeddings, "resampler_attn_out", -1); - } - // layernorm - embeddings = build_norm(embeddings, model.mm_model_ln_post_w, model.mm_model_ln_post_b, NORM_TYPE_NORMAL, eps, -1); - - // projection - embeddings = ggml_mul_mat(ctx0, model.mm_model_proj, embeddings); - - // build the graph - ggml_build_forward_expand(gf, embeddings); - - return gf; - } - - ggml_cgraph * build_internvl() { - GGML_ASSERT(model.class_embedding != nullptr); - GGML_ASSERT(model.position_embeddings != nullptr); - - const int n_pos = n_patches + 1; - ggml_tensor * inp = build_inp(); - - // add CLS token - inp = ggml_concat(ctx0, inp, model.class_embedding, 1); - - // The larger models use a different ViT, which uses RMS norm instead of layer norm - // ref: https://github.com/ggml-org/llama.cpp/pull/13443#issuecomment-2869786188 - norm_type norm_t = (hparams.n_embd == 3200 && hparams.n_layer == 45) - ? NORM_TYPE_RMS // 6B ViT (Used by InternVL 2.5/3 - 26B, 38B, 78B) - : NORM_TYPE_NORMAL; // 300M ViT (Used by all smaller InternVL models) - - ggml_tensor * cur = build_vit( - inp, n_pos, - norm_t, - hparams.ffn_op, - model.position_embeddings, - nullptr); - - // remove CLS token - cur = ggml_view_2d(ctx0, cur, - n_embd, n_patches, - ggml_row_size(cur->type, n_embd), 0); - - // pixel shuffle - { - const int scale_factor = model.hparams.n_merge; - const int bsz = 1; // batch size, always 1 for now since we don't support batching - const int height = n_patches_y; - const int width = n_patches_x; - GGML_ASSERT(scale_factor > 0); - cur = ggml_reshape_4d(ctx0, cur, n_embd * scale_factor, height / scale_factor, width, bsz); - cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); - cur = ggml_cont_4d(ctx0, cur, - n_embd * scale_factor * scale_factor, - height / scale_factor, - width / scale_factor, - bsz); - cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); - // flatten to 2D - cur = ggml_cont_2d(ctx0, cur, - n_embd * scale_factor * scale_factor, - cur->ne[1] * cur->ne[2]); - } - - // projector (always using GELU activation) - { - // projector LayerNorm uses pytorch's default eps = 1e-5 - // ref: https://huggingface.co/OpenGVLab/InternVL3-8B-Instruct/blob/a34d3e4e129a5856abfd6aa6de79776484caa14e/modeling_internvl_chat.py#L79 - cur = build_norm(cur, model.mm_0_w, model.mm_0_b, NORM_TYPE_NORMAL, 1e-5, -1); - cur = ggml_mul_mat(ctx0, model.mm_1_w, cur); - cur = ggml_add(ctx0, cur, model.mm_1_b); - cur = ggml_gelu(ctx0, cur); - cur = ggml_mul_mat(ctx0, model.mm_3_w, cur); - cur = ggml_add(ctx0, cur, model.mm_3_b); - } - - // build the graph - ggml_build_forward_expand(gf, cur); - - return gf; - } - - ggml_cgraph * build_llama4() { - GGML_ASSERT(model.class_embedding != nullptr); - GGML_ASSERT(model.position_embeddings != nullptr); - - const int n_pos = n_patches + 1; // +1 for [CLS] - - // 2D input positions - ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); - ggml_set_name(pos_h, "pos_h"); - ggml_set_input(pos_h); - - ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); - ggml_set_name(pos_w, "pos_w"); - ggml_set_input(pos_w); - - ggml_tensor * inp = build_inp_raw(); - - // Llama4UnfoldConvolution - { - ggml_tensor * kernel = ggml_reshape_4d(ctx0, model.patch_embeddings_0, - patch_size, patch_size, 3, n_embd); - inp = ggml_im2col(ctx0, kernel, inp, patch_size, patch_size, 0, 0, 1, 1, true, inp->type); - inp = ggml_mul_mat(ctx0, model.patch_embeddings_0, inp); - inp = ggml_reshape_2d(ctx0, inp, n_embd, n_patches); - cb(inp, "patch_conv", -1); - } - - // add CLS token - inp = ggml_concat(ctx0, inp, model.class_embedding, 1); - - // build ViT with 2D position embeddings - auto add_pos = [&](ggml_tensor * cur, const clip_layer &) { - // first half is X axis and second half is Y axis - // ref: https://github.com/huggingface/transformers/blob/40a493c7ed4f19f08eadb0639cf26d49bfa5e180/src/transformers/models/llama4/modeling_llama4.py#L1312 - // ref: https://github.com/Blaizzy/mlx-vlm/blob/a57156aa87b33cca6e5ee6cfc14dd4ef8f611be6/mlx_vlm/models/llama4/vision.py#L441 - return build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false); - }; - ggml_tensor * cur = build_vit( - inp, n_pos, - NORM_TYPE_NORMAL, - hparams.ffn_op, - model.position_embeddings, - add_pos); - - // remove CLS token - cur = ggml_view_2d(ctx0, cur, - n_embd, n_patches, - ggml_row_size(cur->type, n_embd), 0); - - // pixel shuffle - // based on Llama4VisionPixelShuffleMLP - // https://github.com/huggingface/transformers/blob/2932f318a20d9e54cc7aea052e040164d85de7d6/src/transformers/models/llama4/modeling_llama4.py#L1151 - { - const int scale_factor = model.hparams.n_merge; - const int bsz = 1; // batch size, always 1 for now since we don't support batching - GGML_ASSERT(scale_factor > 0); - GGML_ASSERT(n_patches_x == n_patches_y); // llama4 only supports square images - cur = ggml_reshape_4d(ctx0, cur, - n_embd * scale_factor, - n_patches_x / scale_factor, - n_patches_y, - bsz); - cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); - cur = ggml_cont_4d(ctx0, cur, - n_embd * scale_factor * scale_factor, - n_patches_x / scale_factor, - n_patches_y / scale_factor, - bsz); - //cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); - // flatten to 2D - cur = ggml_cont_2d(ctx0, cur, - n_embd * scale_factor * scale_factor, - n_patches / scale_factor / scale_factor); - cb(cur, "pixel_shuffle", -1); - } - - // based on Llama4VisionMLP2 (always uses GELU activation, no bias) - { - cur = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w, cur); - cur = ggml_gelu(ctx0, cur); - cur = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, cur); - cur = ggml_gelu(ctx0, cur); - cb(cur, "adapter_mlp", -1); - } - - // Llama4MultiModalProjector - cur = ggml_mul_mat(ctx0, model.mm_model_proj, cur); - cb(cur, "projected", -1); - - // build the graph - ggml_build_forward_expand(gf, cur); - - return gf; - } - - ggml_cgraph * build_kimivl() { - // 2D input positions - ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); - ggml_set_name(pos_h, "pos_h"); - ggml_set_input(pos_h); - - ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); - ggml_set_name(pos_w, "pos_w"); - ggml_set_input(pos_w); - - ggml_tensor * learned_pos_embd = resize_position_embeddings(); - - // build ViT with 2D position embeddings - auto add_pos = [&](ggml_tensor * cur, const clip_layer &) { - // first half is X axis and second half is Y axis - return build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false); - }; - - ggml_tensor * inp = build_inp(); - ggml_tensor * cur = build_vit( - inp, n_patches, - NORM_TYPE_NORMAL, - hparams.ffn_op, - learned_pos_embd, - add_pos); - - cb(cur, "vit_out", -1); - - { - // patch_merger - const int scale_factor = model.hparams.n_merge; - cur = build_patch_merge_permute(cur, scale_factor); - - // projection norm - int proj_inp_dim = cur->ne[0]; - cur = ggml_view_2d(ctx0, cur, - n_embd, cur->ne[1] * scale_factor * scale_factor, - ggml_row_size(cur->type, n_embd), 0); - cur = ggml_norm(ctx0, cur, 1e-5); // default nn.LayerNorm - cur = ggml_mul(ctx0, cur, model.mm_input_norm_w); - cur = ggml_add(ctx0, cur, model.mm_input_norm_b); - cur = ggml_view_2d(ctx0, cur, - proj_inp_dim, cur->ne[1] / scale_factor / scale_factor, - ggml_row_size(cur->type, proj_inp_dim), 0); - cb(cur, "proj_inp_normed", -1); - - // projection mlp - cur = ggml_mul_mat(ctx0, model.mm_1_w, cur); - cur = ggml_add(ctx0, cur, model.mm_1_b); - cur = ggml_gelu(ctx0, cur); - cur = ggml_mul_mat(ctx0, model.mm_2_w, cur); - cur = ggml_add(ctx0, cur, model.mm_2_b); - cb(cur, "proj_out", -1); - } - - // build the graph - ggml_build_forward_expand(gf, cur); - - return gf; - } - - // this graph is used by llava, granite and glm - // due to having embedding_stack (used by granite), we cannot reuse build_vit - ggml_cgraph * build_llava() { - const int batch_size = 1; - const int n_pos = n_patches + (model.class_embedding ? 1 : 0); - - GGML_ASSERT(n_patches_x == n_patches_y && "only square images supported"); - - // Calculate the deepest feature layer based on hparams and projector type - int max_feature_layer = n_layer; - { - // Get the index of the second to last layer; this is the default for models that have a llava projector - int il_last = hparams.n_layer - 1; - int deepest_feature_layer = -1; - - if (ctx->proj_type() == PROJECTOR_TYPE_MINICPMV || ctx->proj_type() == PROJECTOR_TYPE_GLM_EDGE) { - il_last += 1; - } - - // If we set explicit vision feature layers, only go up to the deepest one - // NOTE: only used by granite-vision models for now - for (const auto & feature_layer : hparams.vision_feature_layer) { - if (feature_layer > deepest_feature_layer) { - deepest_feature_layer = feature_layer; - } - } - max_feature_layer = deepest_feature_layer < 0 ? il_last : deepest_feature_layer; - } - - ggml_tensor * inp = build_inp(); - - // concat class_embeddings and patch_embeddings - if (model.class_embedding) { - inp = ggml_concat(ctx0, inp, model.class_embedding, 1); - } - - ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); - ggml_set_name(positions, "positions"); - ggml_set_input(positions); - - inp = ggml_add(ctx0, inp, ggml_get_rows(ctx0, model.position_embeddings, positions)); - - ggml_tensor * inpL = inp; - - // pre-layernorm - if (model.pre_ln_w) { - inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, NORM_TYPE_NORMAL, eps, -1); - cb(inpL, "pre_ln", -1); - } - - std::vector embedding_stack; - const auto & vision_feature_layer = hparams.vision_feature_layer; - - // loop over layers - for (int il = 0; il < max_feature_layer; il++) { - auto & layer = model.layers[il]; - ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states - - // If this is an embedding feature layer, save the output. - // NOTE: 0 index here refers to the input to the encoder. - if (vision_feature_layer.find(il) != vision_feature_layer.end()) { - embedding_stack.push_back(cur); - } - - // layernorm1 - cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il); - cb(cur, "layer_inp_normed", il); - - // self-attention - { - ggml_tensor * Qcur = ggml_mul_mat(ctx0, layer.q_w, cur); - if (layer.q_b) { - Qcur = ggml_add(ctx0, Qcur, layer.q_b); - } - - ggml_tensor * Kcur = ggml_mul_mat(ctx0, layer.k_w, cur); - if (layer.k_b) { - Kcur = ggml_add(ctx0, Kcur, layer.k_b); - } - - ggml_tensor * Vcur = ggml_mul_mat(ctx0, layer.v_w, cur); - if (layer.v_b) { - Vcur = ggml_add(ctx0, Vcur, layer.v_b); - } - - Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos); - Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos); - Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos); - - cb(Qcur, "Qcur", il); - cb(Kcur, "Kcur", il); - cb(Vcur, "Vcur", il); - - cur = build_attn(layer.o_w, layer.o_b, - Qcur, Kcur, Vcur, nullptr, kq_scale, il); - cb(cur, "attn_out", il); - } - - // re-add the layer input, e.g., residual - cur = ggml_add(ctx0, cur, inpL); - - inpL = cur; // inpL = residual, cur = hidden_states - - cb(cur, "ffn_inp", il); - - // layernorm2 - cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il); - cb(cur, "ffn_inp_normed", il); - - // ffn - cur = build_ffn(cur, - layer.ff_up_w, layer.ff_up_b, - layer.ff_gate_w, layer.ff_gate_b, - layer.ff_down_w, layer.ff_down_b, - hparams.ffn_op, il); - - cb(cur, "ffn_out", il); - - // residual 2 - cur = ggml_add(ctx0, inpL, cur); - cb(cur, "layer_out", il); - - inpL = cur; - } - - // post-layernorm - if (model.post_ln_w) { - inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, NORM_TYPE_NORMAL, eps, -1); - } - - ggml_tensor * embeddings = inpL; - - // process vision feature layers (used by granite) - { - // final layer is a vision feature layer - if (vision_feature_layer.find(max_feature_layer) != vision_feature_layer.end()) { - embedding_stack.push_back(inpL); - } - - // If feature layers are explicitly set, stack them (if we have multiple) - if (!embedding_stack.empty()) { - embeddings = embedding_stack[0]; - for (size_t i = 1; i < embedding_stack.size(); i++) { - embeddings = ggml_concat(ctx0, embeddings, embedding_stack[i], 0); - } - } - } - - // llava projector (also used by granite) - if (ctx->model.hparams.has_llava_projector) { - embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]); - - ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); - ggml_set_name(patches, "patches"); - ggml_set_input(patches); - - // shape [1, 576, 1024] - // ne is whcn, ne = [1024, 576, 1, 1] - embeddings = ggml_get_rows(ctx0, embeddings, patches); - - // print_tensor_info(embeddings, "embeddings"); - - // llava projector - if (ctx->proj_type() == PROJECTOR_TYPE_MLP) { - embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); - - embeddings = ggml_gelu(ctx0, embeddings); - if (model.mm_2_w) { - embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_2_b); - } - } - else if (ctx->proj_type() == PROJECTOR_TYPE_MLP_NORM) { - embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); - // ggml_tensor_printf(embeddings, "mm_0_w",0,true,false); - // First LayerNorm - embeddings = ggml_norm(ctx0, embeddings, eps); - embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_1_w), - model.mm_1_b); - - // GELU activation - embeddings = ggml_gelu(ctx0, embeddings); - - // Second linear layer - embeddings = ggml_mul_mat(ctx0, model.mm_3_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_3_b); - - // Second LayerNorm - embeddings = ggml_norm(ctx0, embeddings, eps); - embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_4_w), - model.mm_4_b); - } - else if (ctx->proj_type() == PROJECTOR_TYPE_LDP) { - // MobileVLM projector - int n_patch = 24; - ggml_tensor * mlp_1 = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w, embeddings); - mlp_1 = ggml_add(ctx0, mlp_1, model.mm_model_mlp_1_b); - mlp_1 = ggml_gelu(ctx0, mlp_1); - ggml_tensor * mlp_3 = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, mlp_1); - mlp_3 = ggml_add(ctx0, mlp_3, model.mm_model_mlp_3_b); - // mlp_3 shape = [1, 576, 2048], ne = [2048, 576, 1, 1] - - // block 1 - ggml_tensor * block_1 = nullptr; - { - // transpose from [1, 576, 2048] --> [1, 2048, 576] --> [1, 2048, 24, 24] - mlp_3 = ggml_permute(ctx0, mlp_3, 1, 0, 2, 3); - mlp_3 = ggml_cont_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]); - // stride = 1, padding = 1, bias is nullptr - block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1); - - // layer norm - // // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3)); - // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1] - block_1 = ggml_norm(ctx0, block_1, eps); - block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_0_1_w), model.mm_model_block_1_block_0_1_b); - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3)); - - // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] - // hardswish - ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1); - - block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0); - // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1] - // pointwise conv - block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]); - block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc1_w, block_1); - block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc1_b); - block_1 = ggml_relu(ctx0, block_1); - block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc2_w, block_1); - block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc2_b); - block_1 = ggml_hardsigmoid(ctx0, block_1); - // block_1_hw shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1], block_1 shape = [1, 2048], ne = [2048, 1, 1, 1] - block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]); - block_1 = ggml_mul(ctx0, block_1_hw, block_1); - - int w = block_1->ne[0], h = block_1->ne[1]; - block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]); - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3)); - - // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1] - block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_2_0_w, block_1); - block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]); - - // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1] - block_1 = ggml_norm(ctx0, block_1, eps); - block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_2_1_w), model.mm_model_block_1_block_2_1_b); - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3)); - // block1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] - // residual - block_1 = ggml_add(ctx0, mlp_3, block_1); - } - - // block_2 - { - // stride = 2 - block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1); - - // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1] - // layer norm - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3)); - // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1] - block_1 = ggml_norm(ctx0, block_1, eps); - block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_0_1_w), model.mm_model_block_2_block_0_1_b); - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3)); - // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1] - // hardswish - ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1); - - // not sure the parameters is right for globalAvgPooling - block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0); - // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1] - // pointwise conv - block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]); - block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc1_w, block_1); - block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc1_b); - block_1 = ggml_relu(ctx0, block_1); - block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc2_w, block_1); - block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc2_b); - block_1 = ggml_hardsigmoid(ctx0, block_1); - - // block_1_hw shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1], block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1] - block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]); - block_1 = ggml_mul(ctx0, block_1_hw, block_1); - - int w = block_1->ne[0], h = block_1->ne[1]; - block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]); - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3)); - // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1] - block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_2_0_w, block_1); - block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]); - - - // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1] - block_1 = ggml_norm(ctx0, block_1, eps); - block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_2_1_w), model.mm_model_block_2_block_2_1_b); - block_1 = ggml_reshape_3d(ctx0, block_1, block_1->ne[0], block_1->ne[1] * block_1->ne[2], block_1->ne[3]); - // block_1 shape = [1, 144, 2048], ne = [2048, 144, 1] - } - embeddings = block_1; - } - else if (ctx->proj_type() == PROJECTOR_TYPE_LDPV2) - { - int n_patch = 24; - ggml_tensor * mlp_0 = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); - mlp_0 = ggml_add(ctx0, mlp_0, model.mm_model_mlp_0_b); - mlp_0 = ggml_gelu(ctx0, mlp_0); - ggml_tensor * mlp_2 = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, mlp_0); - mlp_2 = ggml_add(ctx0, mlp_2, model.mm_model_mlp_2_b); - // mlp_2 ne = [2048, 576, 1, 1] - // // AVG Pool Layer 2*2, strides = 2 - mlp_2 = ggml_permute(ctx0, mlp_2, 1, 0, 2, 3); - // mlp_2 ne = [576, 2048, 1, 1] - mlp_2 = ggml_cont_4d(ctx0, mlp_2, n_patch, n_patch, mlp_2->ne[1], mlp_2->ne[2]); - // mlp_2 ne [24, 24, 2048, 1] - mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0); - // weight ne = [3, 3, 2048, 1] - ggml_tensor * peg_0 = ggml_conv_2d_dw(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1); - peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3)); - peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b); - mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3)); - peg_0 = ggml_add(ctx0, peg_0, mlp_2); - peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]); - embeddings = peg_0; - } - else { - GGML_ABORT("fatal error"); - } - } - - // glm projector - else if (ctx->proj_type() == PROJECTOR_TYPE_GLM_EDGE) { - size_t gridsz = (size_t)sqrt(embeddings->ne[1]); - embeddings = ggml_permute(ctx0,embeddings,1,0,2,3); - embeddings = ggml_cont_3d(ctx0, embeddings, gridsz, gridsz, embeddings->ne[1]); - embeddings = ggml_conv_2d(ctx0, model.mm_model_adapter_conv_w, embeddings, 2, 2, 0, 0, 1, 1); - embeddings = ggml_reshape_3d(ctx0, embeddings,embeddings->ne[0]*embeddings->ne[1] , embeddings->ne[2], batch_size); - embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings, 1, 0, 2, 3)); - embeddings = ggml_add(ctx0, embeddings, model.mm_model_adapter_conv_b); - // GLU - { - embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); - embeddings = ggml_norm(ctx0, embeddings, eps); - embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_q_w), model.mm_model_ln_q_b); - embeddings = ggml_gelu_inplace(ctx0, embeddings); - ggml_tensor * x = embeddings; - embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, embeddings); - x = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w,x); - embeddings = ggml_swiglu_split(ctx0, embeddings, x); - embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, embeddings); - } - // arrangement of BOI/EOI token embeddings - // note: these embeddings are not present in text model, hence we cannot process them as text tokens - // see: https://huggingface.co/THUDM/glm-edge-v-2b/blob/main/siglip.py#L53 - { - embeddings = ggml_concat(ctx0, model.mm_boi, embeddings, 1); // BOI - embeddings = ggml_concat(ctx0, embeddings, model.mm_eoi, 1); // EOI - } - } - - else { - GGML_ABORT("llava: unknown projector type"); - } - - // build the graph - ggml_build_forward_expand(gf, embeddings); - - return gf; - } - // whisper encoder with custom projector - ggml_cgraph * build_whisper_enc() { - const int n_frames = img.nx; - const int n_pos = n_frames / 2; - GGML_ASSERT(model.position_embeddings->ne[1] >= n_pos); - - ggml_tensor * inp = build_inp_raw(1); - - // conv1d block - { - // convolution + gelu - ggml_tensor * cur = ggml_conv_1d_ph(ctx0, model.conv1d_1_w, inp, 1, 1); - cur = ggml_add(ctx0, cur, model.conv1d_1_b); - - cur = ggml_gelu_erf(ctx0, cur); - - cur = ggml_conv_1d_ph(ctx0, model.conv1d_2_w, cur, 2, 1); - cur = ggml_add(ctx0, cur, model.conv1d_2_b); - - cur = ggml_gelu_erf(ctx0, cur); - // transpose - inp = ggml_cont(ctx0, ggml_transpose(ctx0, cur)); - cb(inp, "after_conv1d", -1); - } - - // sanity check (only check one layer, but it should be the same for all) - GGML_ASSERT(model.layers[0].ln_1_w && model.layers[0].ln_1_b); - GGML_ASSERT(model.layers[0].ln_2_w && model.layers[0].ln_2_b); - GGML_ASSERT(model.layers[0].q_b); - GGML_ASSERT(model.layers[0].v_b); - GGML_ASSERT(!model.layers[0].k_b); // no bias for k - GGML_ASSERT(model.post_ln_w && model.post_ln_b); - - ggml_tensor * pos_embd_selected = ggml_view_2d( - ctx0, model.position_embeddings, - model.position_embeddings->ne[0], n_pos, - model.position_embeddings->nb[1], 0 - ); - ggml_tensor * cur = build_vit( - inp, n_pos, - NORM_TYPE_NORMAL, - hparams.ffn_op, - pos_embd_selected, - nullptr); - - cb(cur, "after_transformer", -1); - - if (model.audio_has_stack_frames()) { - // StackAudioFrames - // https://huggingface.co/fixie-ai/ultravox-v0_5-llama-3_2-1b/blob/main/ultravox_model.py - int64_t stride = n_embd * hparams.proj_stack_factor; - int64_t padded_len = GGML_PAD(ggml_nelements(cur), stride); - int64_t pad = padded_len - ggml_nelements(cur); - if (pad > 0) { - cur = ggml_view_1d(ctx0, cur, ggml_nelements(cur), 0); - cur = ggml_pad(ctx0, cur, pad, 0, 0, 0); - } - cur = ggml_view_2d(ctx0, cur, stride, padded_len / stride, - ggml_row_size(cur->type, stride), 0); - cb(cur, "after_stacked", -1); - } - - if (ctx->proj_type() == PROJECTOR_TYPE_ULTRAVOX) { - // UltravoxProjector - // pre-norm - cur = ggml_rms_norm(ctx0, cur, 1e-6); - cur = ggml_mul(ctx0, cur, model.mm_norm_pre_w); - - // ffn in - cur = ggml_mul_mat(ctx0, model.mm_1_w, cur); - - // swiglu - // see SwiGLU in ultravox_model.py, the second half passed through is silu, not the first half - cur = ggml_swiglu_swapped(ctx0, cur); - - // mid-norm - cur = ggml_rms_norm(ctx0, cur, 1e-6); - cur = ggml_mul(ctx0, cur, model.mm_norm_mid_w); - - // ffn out - cur = ggml_mul_mat(ctx0, model.mm_2_w, cur); - - } else if (ctx->proj_type() == PROJECTOR_TYPE_QWEN2A) { - // projector - cur = ggml_mul_mat(ctx0, model.mm_fc_w, cur); - cur = ggml_add(ctx0, cur, model.mm_fc_b); - - } else if (ctx->proj_type() == PROJECTOR_TYPE_VOXTRAL) { - // projector - cur = ggml_mul_mat(ctx0, model.mm_1_w, cur); - cur = ggml_gelu_erf(ctx0, cur); - cur = ggml_mul_mat(ctx0, model.mm_2_w, cur); - - } else { - GGML_ABORT("%s: unknown projector type", __func__); - } - - cb(cur, "projected", -1); - - ggml_build_forward_expand(gf, cur); - - return gf; - } - - // cogvlm vision encoder - ggml_cgraph * build_cogvlm() { - GGML_ASSERT(model.class_embedding != nullptr); - GGML_ASSERT(model.position_embeddings != nullptr); - - const int n_pos = n_patches + 1; // +1 for [CLS] - - // build input and concatenate class embedding - ggml_tensor * inp = build_inp(); - inp = ggml_concat(ctx0, inp, model.class_embedding, 1); - - inp = ggml_add(ctx0, inp, model.position_embeddings); - cb(inp, "inp_pos", -1); - - ggml_tensor * inpL = inp; - - for (int il = 0; il < n_layer; il++) { - auto & layer = model.layers[il]; - ggml_tensor * cur = inpL; - - cur = ggml_mul_mat(ctx0, layer.qkv_w, cur); - - cur = ggml_add(ctx0, cur, layer.qkv_b); - - ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float), - cur->nb[1], 0); - ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float), - cur->nb[1], n_embd * sizeof(float)); - ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float), - cur->nb[1], 2 * n_embd * sizeof(float)); - - cb(Qcur, "Qcur", il); - cb(Kcur, "Kcur", il); - cb(Vcur, "Vcur", il); - - cur = build_attn(layer.o_w, layer.o_b, - Qcur, Kcur, Vcur, nullptr, kq_scale, il); - cb(cur, "attn_out", il); - - cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il); - cb(cur, "attn_post_norm", il); - - cur = ggml_add(ctx0, cur, inpL); - inpL = cur; - - cur = build_ffn(cur, - layer.ff_up_w, layer.ff_up_b, - layer.ff_gate_w, layer.ff_gate_b, - layer.ff_down_w, layer.ff_down_b, - hparams.ffn_op, il); - - cb(cur, "ffn_out", il); - - cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il); - cb(cur, "ffn_post_norm", il); - - cur = ggml_add(ctx0, cur, inpL); - cb(cur, "layer_out", il); - inpL = cur; - - } - - // remove CLS token (like build_llama4 does) - ggml_tensor * cur = ggml_view_2d(ctx0, inpL, - n_embd, n_patches, - ggml_row_size(inpL->type, n_embd), 0); - - // Multiply with mm_model_proj - cur = ggml_mul_mat(ctx0, model.mm_model_proj, cur); - - // Apply layernorm, weight, bias - cur = build_norm(cur, model.mm_post_fc_norm_w, model.mm_post_fc_norm_b, NORM_TYPE_NORMAL, 1e-5, -1); - - // Apply GELU - cur = ggml_gelu_inplace(ctx0, cur); - - // Branch 1: multiply with mm_h_to_4h_w - ggml_tensor * h_to_4h = ggml_mul_mat(ctx0, model.mm_h_to_4h_w, cur); - - // Branch 2: multiply with mm_gate_w - ggml_tensor * gate = ggml_mul_mat(ctx0, model.mm_gate_w, cur); - - // Apply silu - gate = ggml_swiglu_split(ctx0, gate, h_to_4h); - - // Apply mm_4h_to_h_w - cur = ggml_mul_mat(ctx0, model.mm_4h_to_h_w, gate); - - // Concatenate with boi and eoi - cur = ggml_concat(ctx0, model.mm_boi, cur, 1); - cur = ggml_concat(ctx0, cur, model.mm_eoi, 1); - - // build the graph - ggml_build_forward_expand(gf, cur); - - return gf; - } - -private: - // - // utility functions - // - - void cb(ggml_tensor * cur0, const char * name, int il) const { - if (ctx->debug_graph) { - ggml_tensor * cur = ggml_cpy(ctx0, cur0, ggml_dup_tensor(ctx0, cur0)); - std::string cur_name = il >= 0 ? std::string(name) + "_" + std::to_string(il) : name; - ggml_set_name(cur, cur_name.c_str()); - ggml_set_output(cur); - ggml_build_forward_expand(gf, cur); - ctx->debug_print_tensors.push_back(cur); - } - } - - // siglip2 naflex - ggml_tensor * resize_position_embeddings() { - ggml_tensor * pos_embd = model.position_embeddings; - const int height = img.ny / patch_size; - const int width = img.nx / patch_size; - const uint32_t mode = GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ANTIALIAS; - const int n_per_side = (int)std::sqrt(pos_embd->ne[1]); - - GGML_ASSERT(pos_embd); - - if (height == n_per_side && width == n_per_side) { - return pos_embd; - } - - pos_embd = ggml_reshape_3d(ctx0, pos_embd, n_embd, n_per_side, n_per_side); // -> (n_embd, n_per_side, n_per_side) - pos_embd = ggml_permute(ctx0, pos_embd, 2, 0, 1, 3); // -> (n_per_side, n_per_side, n_embd) - pos_embd = ggml_interpolate(ctx0, pos_embd, width, height, n_embd, 1, mode); // -> (width, height, n_embd) - pos_embd = ggml_permute(ctx0, pos_embd, 1, 2, 0, 3); // -> (n_embd, width, height) - pos_embd = ggml_cont_2d(ctx0, pos_embd, n_embd, width * height); // -> (n_embd, width * height) + GGML_ASSERT(pos_embd); + if (height == n_per_side && width == n_per_side) { return pos_embd; } - // build vision transformer (ViT) cgraph - // this function should cover most of the models - // if your model has specific features, you should probably duplicate this function - ggml_tensor * build_vit( - ggml_tensor * inp, - int64_t n_pos, - norm_type norm_t, - ffn_op_type ffn_t, - ggml_tensor * learned_pos_embd, - std::function add_pos - ) { - if (learned_pos_embd) { - inp = ggml_add(ctx0, inp, learned_pos_embd); - cb(inp, "pos_embed", -1); - } + pos_embd = ggml_reshape_3d(ctx0, pos_embd, n_embd, n_per_side, n_per_side); // -> (n_embd, n_per_side, n_per_side) + pos_embd = ggml_permute(ctx0, pos_embd, 2, 0, 1, 3); // -> (n_per_side, n_per_side, n_embd) + pos_embd = ggml_interpolate(ctx0, pos_embd, width, height, n_embd, 1, mode); // -> (width, height, n_embd) + pos_embd = ggml_permute(ctx0, pos_embd, 1, 2, 0, 3); // -> (n_embd, width, height) + pos_embd = ggml_cont_2d(ctx0, pos_embd, n_embd, width * height); // -> (n_embd, width * height) - ggml_tensor * inpL = inp; + return pos_embd; +} - // pre-layernorm - if (model.pre_ln_w) { - inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1); - cb(inpL, "pre_ln", -1); - } +// build vision transformer (ViT) cgraph +// this function should cover most of the models +// if your model has specific features, you should probably duplicate this function +ggml_tensor * clip_graph::build_vit( + ggml_tensor * inp, + int64_t n_pos, + norm_type norm_t, + ffn_op_type ffn_t, + ggml_tensor * learned_pos_embd, + std::function add_pos + ) { + if (learned_pos_embd) { + inp = ggml_add(ctx0, inp, learned_pos_embd); + cb(inp, "pos_embed", -1); + } - // loop over layers - for (int il = 0; il < n_layer; il++) { - auto & layer = model.layers[il]; - ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states + ggml_tensor * inpL = inp; - // layernorm1 - cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il); - cb(cur, "layer_inp_normed", il); + // pre-layernorm + if (model.pre_ln_w) { + inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1); + cb(inpL, "pre_ln", -1); + } - // self-attention - { - ggml_tensor * Qcur = ggml_mul_mat(ctx0, layer.q_w, cur); + // loop over layers + for (int il = 0; il < n_layer; il++) { + auto & layer = model.layers[il]; + ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states + + // layernorm1 + cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il); + cb(cur, "layer_inp_normed", il); + + // self-attention + { + ggml_tensor * Qcur = nullptr; + ggml_tensor * Kcur = nullptr; + ggml_tensor * Vcur = nullptr; + if (layer.qkv_w != nullptr) { + // fused qkv + cur = ggml_mul_mat(ctx0, layer.qkv_w, cur); + if (layer.qkv_b != nullptr) { + cur = ggml_add(ctx0, cur, layer.qkv_b); + } + + Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, + /* nb1 */ ggml_row_size(cur->type, d_head), + /* nb2 */ cur->nb[1], + /* offset */ 0); + + Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, + /* nb1 */ ggml_row_size(cur->type, d_head), + /* nb2 */ cur->nb[1], + /* offset */ ggml_row_size(cur->type, n_embd)); + + Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, + /* nb1 */ ggml_row_size(cur->type, d_head), + /* nb2 */ cur->nb[1], + /* offset */ ggml_row_size(cur->type, 2 * n_embd)); + + // TODO: q/k norm requires row size == n_embd, while here it's d_head + // we can add support in the future if needed + GGML_ASSERT(layer.q_norm == nullptr && layer.k_norm == nullptr); + + } else { + // separate q, k, v + Qcur = ggml_mul_mat(ctx0, layer.q_w, cur); if (layer.q_b) { Qcur = ggml_add(ctx0, Qcur, layer.q_b); } - ggml_tensor * Kcur = ggml_mul_mat(ctx0, layer.k_w, cur); + Kcur = ggml_mul_mat(ctx0, layer.k_w, cur); if (layer.k_b) { Kcur = ggml_add(ctx0, Kcur, layer.k_b); } - ggml_tensor * Vcur = ggml_mul_mat(ctx0, layer.v_w, cur); + Vcur = ggml_mul_mat(ctx0, layer.v_w, cur); if (layer.v_b) { Vcur = ggml_add(ctx0, Vcur, layer.v_b); } @@ -2102,445 +380,479 @@ private: Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos); Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos); Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos); - - cb(Qcur, "Qcur", il); - cb(Kcur, "Kcur", il); - cb(Vcur, "Vcur", il); - - if (add_pos) { - Qcur = add_pos(Qcur, layer); - Kcur = add_pos(Kcur, layer); - cb(Qcur, "Qcur_pos", il); - cb(Kcur, "Kcur_pos", il); - } - - cur = build_attn(layer.o_w, layer.o_b, - Qcur, Kcur, Vcur, nullptr, kq_scale, il); - cb(cur, "attn_out", il); } - if (layer.ls_1_w) { - cur = ggml_mul(ctx0, cur, layer.ls_1_w); - cb(cur, "attn_out_scaled", il); + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + if (add_pos) { + Qcur = add_pos(Qcur, layer); + Kcur = add_pos(Kcur, layer); + cb(Qcur, "Qcur_pos", il); + cb(Kcur, "Kcur_pos", il); } - // re-add the layer input, e.g., residual - cur = ggml_add(ctx0, cur, inpL); - - inpL = cur; // inpL = residual, cur = hidden_states - - cb(cur, "ffn_inp", il); - - // layernorm2 - cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il); - cb(cur, "ffn_inp_normed", il); - - // ffn - cur = build_ffn(cur, - layer.ff_up_w, layer.ff_up_b, - layer.ff_gate_w, layer.ff_gate_b, - layer.ff_down_w, layer.ff_down_b, - ffn_t, il); - - cb(cur, "ffn_out", il); - - if (layer.ls_2_w) { - cur = ggml_mul(ctx0, cur, layer.ls_2_w); - cb(cur, "ffn_out_scaled", il); - } - - // residual 2 - cur = ggml_add(ctx0, inpL, cur); - cb(cur, "layer_out", il); - - inpL = cur; + cur = build_attn(layer.o_w, layer.o_b, + Qcur, Kcur, Vcur, nullptr, kq_scale, il); + cb(cur, "attn_out", il); } - if (ctx->model.audio_has_avgpool()) { - ggml_tensor * cur = inpL; - cur = ggml_transpose(ctx0, cur); - cur = ggml_cont(ctx0, cur); - cur = ggml_pool_1d(ctx0, cur, GGML_OP_POOL_AVG, 2, 2, 0); - cur = ggml_transpose(ctx0, cur); - cur = ggml_cont(ctx0, cur); - inpL = cur; + if (layer.ls_1_w) { + cur = ggml_mul(ctx0, cur, layer.ls_1_w); + cb(cur, "attn_out_scaled", il); } - // post-layernorm - if (model.post_ln_w) { - inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, -1); + // re-add the layer input, e.g., residual + cur = ggml_add(ctx0, cur, inpL); + + inpL = cur; // inpL = residual, cur = hidden_states + + cb(cur, "ffn_inp", il); + + // layernorm2 + cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il); + cb(cur, "ffn_inp_normed", il); + + // ffn + cur = build_ffn(cur, + layer.ff_up_w, layer.ff_up_b, + layer.ff_gate_w, layer.ff_gate_b, + layer.ff_down_w, layer.ff_down_b, + ffn_t, il); + + cb(cur, "ffn_out", il); + + if (layer.ls_2_w) { + cur = ggml_mul(ctx0, cur, layer.ls_2_w); + cb(cur, "ffn_out_scaled", il); } - return inpL; + + // residual 2 + cur = ggml_add(ctx0, inpL, cur); + cb(cur, "layer_out", il); + + inpL = cur; } - // build the input after conv2d (inp_raw --> patches) - // returns tensor with shape [n_embd, n_patches] - ggml_tensor * build_inp() { - ggml_tensor * inp_raw = build_inp_raw(); - ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - inp = ggml_reshape_2d(ctx0, inp, n_patches, n_embd); - inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp)); - if (model.patch_bias) { - inp = ggml_add(ctx0, inp, model.patch_bias); - cb(inp, "patch_bias", -1); - } - return inp; + if (model.audio_has_avgpool()) { + ggml_tensor * cur = inpL; + cur = ggml_transpose(ctx0, cur); + cur = ggml_cont(ctx0, cur); + cur = ggml_pool_1d(ctx0, cur, GGML_OP_POOL_AVG, 2, 2, 0); + cur = ggml_transpose(ctx0, cur); + cur = ggml_cont(ctx0, cur); + inpL = cur; } - ggml_tensor * build_inp_raw(int channels = 3) { - ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, img.nx, img.ny, channels); - ggml_set_name(inp_raw, "inp_raw"); - ggml_set_input(inp_raw); - return inp_raw; + // post-layernorm + if (model.post_ln_w) { + inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, -1); } + return inpL; +} - ggml_tensor * build_norm( - ggml_tensor * cur, - ggml_tensor * mw, - ggml_tensor * mb, - norm_type type, - float norm_eps, - int il) const { - - cur = type == NORM_TYPE_RMS - ? ggml_rms_norm(ctx0, cur, norm_eps) - : ggml_norm(ctx0, cur, norm_eps); - - if (mw || mb) { - cb(cur, "norm", il); - } - - if (mw) { - cur = ggml_mul(ctx0, cur, mw); - if (mb) { - cb(cur, "norm_w", il); - } - } - - if (mb) { - cur = ggml_add(ctx0, cur, mb); - } - - return cur; +// build the input after conv2d (inp_raw --> patches) +// returns tensor with shape [n_embd, n_patches] +ggml_tensor * clip_graph::build_inp() { + ggml_tensor * inp_raw = build_inp_raw(); + ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); + inp = ggml_reshape_2d(ctx0, inp, n_patches, n_embd); + inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp)); + if (model.patch_bias) { + inp = ggml_add(ctx0, inp, model.patch_bias); + cb(inp, "patch_bias", -1); } + return inp; +} - ggml_tensor * build_ffn( - ggml_tensor * cur, - ggml_tensor * up, - ggml_tensor * up_b, - ggml_tensor * gate, - ggml_tensor * gate_b, - ggml_tensor * down, - ggml_tensor * down_b, - ffn_op_type type_op, - int il) const { +ggml_tensor * clip_graph::build_inp_raw(int channels) { + ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, img.nx, img.ny, channels); + ggml_set_name(inp_raw, "inp_raw"); + ggml_set_input(inp_raw); + return inp_raw; +} - ggml_tensor * tmp = up ? ggml_mul_mat(ctx0, up, cur) : cur; - cb(tmp, "ffn_up", il); - - if (up_b) { - tmp = ggml_add(ctx0, tmp, up_b); - cb(tmp, "ffn_up_b", il); - } - - if (gate) { - cur = ggml_mul_mat(ctx0, gate, cur); - cb(cur, "ffn_gate", il); - - if (gate_b) { - cur = ggml_add(ctx0, cur, gate_b); - cb(cur, "ffn_gate_b", il); - } - } else { - cur = tmp; - } - - // we only support parallel ffn for now - switch (type_op) { - case FFN_SILU: - if (gate) { - cur = ggml_swiglu_split(ctx0, cur, tmp); - cb(cur, "ffn_swiglu", il); - } else { - cur = ggml_silu(ctx0, cur); - cb(cur, "ffn_silu", il); - } break; - case FFN_GELU: - if (gate) { - cur = ggml_geglu_split(ctx0, cur, tmp); - cb(cur, "ffn_geglu", il); - } else { - cur = ggml_gelu(ctx0, cur); - cb(cur, "ffn_gelu", il); - } break; - case FFN_GELU_ERF: - if (gate) { - cur = ggml_geglu_erf_split(ctx0, cur, tmp); - cb(cur, "ffn_geglu_erf", il); - } else { - cur = ggml_gelu_erf(ctx0, cur); - cb(cur, "ffn_gelu_erf", il); - } break; - case FFN_GELU_QUICK: - if (gate) { - cur = ggml_geglu_quick_split(ctx0, cur, tmp); - cb(cur, "ffn_geglu_quick", il); - } else { - cur = ggml_gelu_quick(ctx0, cur); - cb(cur, "ffn_gelu_quick", il); - } break; - } - - if (down) { - cur = ggml_mul_mat(ctx0, down, cur); - } - - if (down_b) { - cb(cur, "ffn_down", il); - } - - if (down_b) { - cur = ggml_add(ctx0, cur, down_b); - } - - return cur; - } - - ggml_tensor * build_attn( - ggml_tensor * wo, - ggml_tensor * wo_b, - ggml_tensor * q_cur, - ggml_tensor * k_cur, - ggml_tensor * v_cur, - ggml_tensor * kq_mask, - float kq_scale, - int il) const { - // these nodes are added to the graph together so that they are not reordered - // by doing so, the number of splits in the graph is reduced - ggml_build_forward_expand(gf, q_cur); - ggml_build_forward_expand(gf, k_cur); - ggml_build_forward_expand(gf, v_cur); - - ggml_tensor * q = ggml_permute(ctx0, q_cur, 0, 2, 1, 3); - //cb(q, "q", il); - - ggml_tensor * k = ggml_permute(ctx0, k_cur, 0, 2, 1, 3); - //cb(k, "k", il); - - ggml_tensor * cur; - - if (ctx->flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) { - ggml_tensor * v = ggml_permute(ctx0, v_cur, 0, 2, 1, 3); - - k = ggml_cast(ctx0, k, GGML_TYPE_F16); - v = ggml_cast(ctx0, v, GGML_TYPE_F16); - - cur = ggml_flash_attn_ext(ctx0, q, k, v, kq_mask, kq_scale, 0.0f, 0.0f); - ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32); - - cur = ggml_reshape_2d(ctx0, cur, cur->ne[0]*cur->ne[1], cur->ne[2]*cur->ne[3]); - - } else { - ggml_tensor * v = ggml_permute(ctx0, v_cur, 1, 2, 0, 3); - v = ggml_cont(ctx0, v); - - const auto n_tokens = q->ne[1]; - const auto n_head = q->ne[2]; - - ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); - // F32 may not needed for vision encoders? - // ggml_mul_mat_set_prec(kq, GGML_PREC_F32); - - kq = ggml_soft_max_ext(ctx0, kq, kq_mask, kq_scale, 0.0f); - - ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq); - cur = ggml_permute(ctx0, kqv, 0, 2, 1, 3); - cur = ggml_cont_2d(ctx0, cur, cur->ne[0]*n_head, n_tokens); - } - - cb(cur, "kqv_out", il); - - if (wo) { - cur = ggml_mul_mat(ctx0, wo, cur); - } - - if (wo_b) { - cur = ggml_add(ctx0, cur, wo_b); - } - - return cur; - } - - // implementation of the 2D RoPE without adding a new op in ggml - // this is not efficient (use double the memory), but works on all backends - // TODO: there was a more efficient which relies on ggml_view and ggml_rope_ext_inplace, but the rope inplace does not work well with non-contiguous tensors ; we should fix that and revert back to the original implementation in https://github.com/ggml-org/llama.cpp/pull/13065 - static ggml_tensor * build_rope_2d( - ggml_context * ctx0, +ggml_tensor * clip_graph::build_norm( ggml_tensor * cur, - ggml_tensor * pos_a, // first half - ggml_tensor * pos_b, // second half - const float freq_base, - const bool interleave_freq - ) { - const int64_t n_dim = cur->ne[0]; - const int64_t n_head = cur->ne[1]; - const int64_t n_pos = cur->ne[2]; + ggml_tensor * mw, + ggml_tensor * mb, + norm_type type, + float norm_eps, + int il) const { - // for example, if we have cur tensor of shape (n_dim=8, n_head, n_pos) - // we will have a list of 4 inv_freq: 1e-0, 1e-1, 1e-2, 1e-3 - // first half of cur will use 1e-0, 1e-2 (even) - // second half of cur will use 1e-1, 1e-3 (odd) - // the trick here is to rotate just half of n_dim, so inv_freq will automatically be even - // ^ don't ask me why, it's math! -2(2i) / n_dim == -2i / (n_dim/2) - // then for the second half, we use freq_scale to shift the inv_freq - // ^ why? replace (2i) with (2i+1) in the above equation - const float freq_scale_odd = interleave_freq - ? std::pow(freq_base, (float)-2/n_dim) - : 1.0; + cur = type == NORM_TYPE_RMS + ? ggml_rms_norm(ctx0, cur, norm_eps) + : ggml_norm(ctx0, cur, norm_eps); - // first half - ggml_tensor * first; - { - first = ggml_view_3d(ctx0, cur, - n_dim/2, n_head, n_pos, - ggml_row_size(cur->type, n_dim), - ggml_row_size(cur->type, n_dim*n_head), - 0); - first = ggml_rope_ext( - ctx0, - first, - pos_a, // positions - nullptr, // freq factors - n_dim/2, // n_dims - 0, 0, freq_base, - 1.0f, 0.0f, 1.0f, 0.0f, 0.0f - ); + if (mw || mb) { + cb(cur, "norm", il); + } + + if (mw) { + cur = ggml_mul(ctx0, cur, mw); + if (mb) { + cb(cur, "norm_w", il); } + } - // second half - ggml_tensor * second; - { - second = ggml_view_3d(ctx0, cur, - n_dim/2, n_head, n_pos, - ggml_row_size(cur->type, n_dim), - ggml_row_size(cur->type, n_dim*n_head), - n_dim/2 * ggml_element_size(cur)); - second = ggml_rope_ext( - ctx0, - second, - pos_b, // positions - nullptr, // freq factors - n_dim/2, // n_dims - 0, 0, freq_base, - freq_scale_odd, - 0.0f, 1.0f, 0.0f, 0.0f - ); + if (mb) { + cur = ggml_add(ctx0, cur, mb); + } + + return cur; +} + +ggml_tensor * clip_graph::build_ffn( + ggml_tensor * cur, + ggml_tensor * up, + ggml_tensor * up_b, + ggml_tensor * gate, + ggml_tensor * gate_b, + ggml_tensor * down, + ggml_tensor * down_b, + ffn_op_type type_op, + int il) const { + + ggml_tensor * tmp = up ? ggml_mul_mat(ctx0, up, cur) : cur; + cb(tmp, "ffn_up", il); + + if (up_b) { + tmp = ggml_add(ctx0, tmp, up_b); + cb(tmp, "ffn_up_b", il); + } + + if (gate) { + cur = ggml_mul_mat(ctx0, gate, cur); + cb(cur, "ffn_gate", il); + + if (gate_b) { + cur = ggml_add(ctx0, cur, gate_b); + cb(cur, "ffn_gate_b", il); } + } else { + cur = tmp; + } - cur = ggml_concat(ctx0, first, second, 0); + // we only support parallel ffn for now + switch (type_op) { + case FFN_SILU: + if (gate) { + cur = ggml_swiglu_split(ctx0, cur, tmp); + cb(cur, "ffn_swiglu", il); + } else { + cur = ggml_silu(ctx0, cur); + cb(cur, "ffn_silu", il); + } break; + case FFN_GELU: + if (gate) { + cur = ggml_geglu_split(ctx0, cur, tmp); + cb(cur, "ffn_geglu", il); + } else { + cur = ggml_gelu(ctx0, cur); + cb(cur, "ffn_gelu", il); + } break; + case FFN_GELU_ERF: + if (gate) { + cur = ggml_geglu_erf_split(ctx0, cur, tmp); + cb(cur, "ffn_geglu_erf", il); + } else { + cur = ggml_gelu_erf(ctx0, cur); + cb(cur, "ffn_gelu_erf", il); + } break; + case FFN_GELU_QUICK: + if (gate) { + cur = ggml_geglu_quick_split(ctx0, cur, tmp); + cb(cur, "ffn_geglu_quick", il); + } else { + cur = ggml_gelu_quick(ctx0, cur); + cb(cur, "ffn_gelu_quick", il); + } break; + } + + if (down) { + cur = ggml_mul_mat(ctx0, down, cur); + } + + if (down_b) { + cb(cur, "ffn_down", il); + } + + if (down_b) { + cur = ggml_add(ctx0, cur, down_b); + } + + return cur; +} + +ggml_tensor * clip_graph::build_attn( + ggml_tensor * wo, + ggml_tensor * wo_b, + ggml_tensor * q_cur, + ggml_tensor * k_cur, + ggml_tensor * v_cur, + ggml_tensor * kq_mask, + float kq_scale, + int il) const { + // these nodes are added to the graph together so that they are not reordered + // by doing so, the number of splits in the graph is reduced + ggml_build_forward_expand(gf, q_cur); + ggml_build_forward_expand(gf, k_cur); + ggml_build_forward_expand(gf, v_cur); + + ggml_tensor * q = ggml_permute(ctx0, q_cur, 0, 2, 1, 3); + //cb(q, "q", il); + + ggml_tensor * k = ggml_permute(ctx0, k_cur, 0, 2, 1, 3); + //cb(k, "k", il); + + ggml_tensor * cur; + + if (flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) { + ggml_tensor * v = ggml_permute(ctx0, v_cur, 0, 2, 1, 3); + + k = ggml_cast(ctx0, k, GGML_TYPE_F16); + v = ggml_cast(ctx0, v, GGML_TYPE_F16); + + cur = ggml_flash_attn_ext(ctx0, q, k, v, kq_mask, kq_scale, 0.0f, 0.0f); + ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32); + + cur = ggml_reshape_2d(ctx0, cur, cur->ne[0]*cur->ne[1], cur->ne[2]*cur->ne[3]); + + } else { + ggml_tensor * v = ggml_permute(ctx0, v_cur, 1, 2, 0, 3); + v = ggml_cont(ctx0, v); + + const auto n_tokens = q->ne[1]; + const auto n_head = q->ne[2]; + + ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); + // F32 may not needed for vision encoders? + // ggml_mul_mat_set_prec(kq, GGML_PREC_F32); + + kq = ggml_soft_max_ext(ctx0, kq, kq_mask, kq_scale, 0.0f); + + ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq); + cur = ggml_permute(ctx0, kqv, 0, 2, 1, 3); + cur = ggml_cont_2d(ctx0, cur, cur->ne[0]*n_head, n_tokens); + } + + cb(cur, "kqv_out", il); + + if (wo) { + cur = ggml_mul_mat(ctx0, wo, cur); + } + + if (wo_b) { + cur = ggml_add(ctx0, cur, wo_b); + } + + return cur; +} + +// implementation of the 2D RoPE without adding a new op in ggml +// this is not efficient (use double the memory), but works on all backends +// TODO: there was a more efficient which relies on ggml_view and ggml_rope_ext_inplace, but the rope inplace does not work well with non-contiguous tensors ; we should fix that and revert back to the original implementation in https://github.com/ggml-org/llama.cpp/pull/13065 +ggml_tensor * clip_graph::build_rope_2d( + ggml_context * ctx0, + ggml_tensor * cur, + ggml_tensor * pos_a, // first half + ggml_tensor * pos_b, // second half + const float freq_base, + const bool interleave_freq +) { + const int64_t n_dim = cur->ne[0]; + const int64_t n_head = cur->ne[1]; + const int64_t n_pos = cur->ne[2]; + + // for example, if we have cur tensor of shape (n_dim=8, n_head, n_pos) + // we will have a list of 4 inv_freq: 1e-0, 1e-1, 1e-2, 1e-3 + // first half of cur will use 1e-0, 1e-2 (even) + // second half of cur will use 1e-1, 1e-3 (odd) + // the trick here is to rotate just half of n_dim, so inv_freq will automatically be even + // ^ don't ask me why, it's math! -2(2i) / n_dim == -2i / (n_dim/2) + // then for the second half, we use freq_scale to shift the inv_freq + // ^ why? replace (2i) with (2i+1) in the above equation + const float freq_scale_odd = interleave_freq + ? std::pow(freq_base, (float)-2/n_dim) + : 1.0; + + // first half + ggml_tensor * first; + { + first = ggml_view_3d(ctx0, cur, + n_dim/2, n_head, n_pos, + ggml_row_size(cur->type, n_dim), + ggml_row_size(cur->type, n_dim*n_head), + 0); + first = ggml_rope_ext( + ctx0, + first, + pos_a, // positions + nullptr, // freq factors + n_dim/2, // n_dims + 0, 0, freq_base, + 1.0f, 0.0f, 1.0f, 0.0f, 0.0f + ); + } + + // second half + ggml_tensor * second; + { + second = ggml_view_3d(ctx0, cur, + n_dim/2, n_head, n_pos, + ggml_row_size(cur->type, n_dim), + ggml_row_size(cur->type, n_dim*n_head), + n_dim/2 * ggml_element_size(cur)); + second = ggml_rope_ext( + ctx0, + second, + pos_b, // positions + nullptr, // freq factors + n_dim/2, // n_dims + 0, 0, freq_base, + freq_scale_odd, + 0.0f, 1.0f, 0.0f, 0.0f + ); + } + + cur = ggml_concat(ctx0, first, second, 0); + return cur; +} + +// Generic function to stack frames for audio processing +// Abstracts out the StackAudioFrames logic used by ultravox +ggml_tensor * clip_graph::build_stack(ggml_tensor * cur, int32_t stack_factor, int32_t n_embed) { + if (stack_factor <= 1) { return cur; } - // aka pixel_shuffle / pixel_unshuffle / patch_merger (Kimi-VL) - // support dynamic resolution - ggml_tensor * build_patch_merge_permute(ggml_tensor * cur, int scale_factor) { - GGML_ASSERT(scale_factor > 1); + int64_t total_elements = ggml_nelements(cur); + int64_t stride = n_embed * stack_factor; - const int n_embd = cur->ne[0]; - int width = img.nx / patch_size; - int height = img.ny / patch_size; + // Calculate padded length + int64_t padded_len = GGML_PAD(total_elements, stride); + int64_t pad = padded_len - total_elements; - // pad width and height to factor - const int64_t pad_width = CLIP_ALIGN(width, scale_factor) - width; - const int64_t pad_height = CLIP_ALIGN(height, scale_factor) - height; - cur = ggml_reshape_3d(ctx0, cur, n_embd, width, height); - if (pad_width || pad_height) { - cur = ggml_pad(ctx0, cur, 0, pad_width, pad_height, 0); - width += pad_width; - height += pad_height; - } - - // unshuffle h - cur = ggml_reshape_3d(ctx0, cur, n_embd * scale_factor, width / scale_factor, height); - cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); - - // unshuffle w - cur = ggml_cont_3d(ctx0, cur, n_embd * scale_factor * scale_factor, height / scale_factor, width / scale_factor); - cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); - - cur = ggml_cont_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]); - cb(cur, "pixel_shuffle", -1); - - return cur; + if (pad > 0) { + // Pad the tensor to make it divisible by stride + cur = ggml_view_1d(ctx0, cur, total_elements, 0); + cur = ggml_pad(ctx0, cur, pad, 0, 0, 0); } -}; + // Reshape to [stride, padded_len / stride] + cur = ggml_view_2d(ctx0, cur, stride, padded_len / stride, + ggml_row_size(cur->type, stride), 0); + return cur; +} + +// aka pixel_shuffle / pixel_unshuffle / patch_merger (Kimi-VL) +// support dynamic resolution +ggml_tensor * clip_graph::build_patch_merge_permute(ggml_tensor * cur, int scale_factor) { + GGML_ASSERT(scale_factor > 1); + + const int n_embd = cur->ne[0]; + int width = img.nx / patch_size; + int height = img.ny / patch_size; + + // pad width and height to factor + const int64_t pad_width = CLIP_ALIGN(width, scale_factor) - width; + const int64_t pad_height = CLIP_ALIGN(height, scale_factor) - height; + cur = ggml_reshape_3d(ctx0, cur, n_embd, width, height); + if (pad_width || pad_height) { + cur = ggml_pad(ctx0, cur, 0, pad_width, pad_height, 0); + width += pad_width; + height += pad_height; + } + + // unshuffle h + cur = ggml_reshape_3d(ctx0, cur, n_embd * scale_factor, width / scale_factor, height); + cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); + + // unshuffle w + cur = ggml_cont_3d(ctx0, cur, n_embd * scale_factor * scale_factor, height / scale_factor, width / scale_factor); + cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); + + cur = ggml_cont_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]); + cb(cur, "pixel_shuffle", -1); + + return cur; +} static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch & imgs) { GGML_ASSERT(imgs.entries.size() == 1 && "n_batch > 1 is not supported"); - clip_graph graph(ctx, *imgs.entries[0]); - ggml_cgraph * res; + const clip_image_f32 & img = *imgs.entries[0]; + std::unique_ptr builder; switch (ctx->proj_type()) { case PROJECTOR_TYPE_GEMMA3: case PROJECTOR_TYPE_IDEFICS3: case PROJECTOR_TYPE_LFM2: + case PROJECTOR_TYPE_JANUS_PRO: { - res = graph.build_siglip(); + builder = std::make_unique(ctx, img); } break; case PROJECTOR_TYPE_PIXTRAL: case PROJECTOR_TYPE_LIGHTONOCR: { - res = graph.build_pixtral(); + builder = std::make_unique(ctx, img); } break; case PROJECTOR_TYPE_QWEN2VL: case PROJECTOR_TYPE_QWEN25VL: { - res = graph.build_qwen2vl(); + builder = std::make_unique(ctx, img); } break; case PROJECTOR_TYPE_QWEN3VL: { - res = graph.build_qwen3vl(); + builder = std::make_unique(ctx, img); } break; case PROJECTOR_TYPE_MINICPMV: { - res = graph.build_minicpmv(); + builder = std::make_unique(ctx, img); } break; case PROJECTOR_TYPE_INTERNVL: { - res = graph.build_internvl(); + builder = std::make_unique(ctx, img); } break; case PROJECTOR_TYPE_LLAMA4: { - res = graph.build_llama4(); + builder = std::make_unique(ctx, img); } break; case PROJECTOR_TYPE_ULTRAVOX: case PROJECTOR_TYPE_VOXTRAL: case PROJECTOR_TYPE_QWEN2A: + case PROJECTOR_TYPE_GLMA: { - res = graph.build_whisper_enc(); + builder = std::make_unique(ctx, img); } break; case PROJECTOR_TYPE_KIMIVL: { - res = graph.build_kimivl(); - } break; - case PROJECTOR_TYPE_JANUS_PRO: - { - res = graph.build_siglip(); + builder = std::make_unique(ctx, img); } break; case PROJECTOR_TYPE_COGVLM: { - res = graph.build_cogvlm(); + builder = std::make_unique(ctx, img); + } break; + case PROJECTOR_TYPE_MLP: + case PROJECTOR_TYPE_MLP_NORM: + case PROJECTOR_TYPE_LDP: + case PROJECTOR_TYPE_LDPV2: + case PROJECTOR_TYPE_GLM_EDGE: + { + builder = std::make_unique(ctx, img); } break; default: - { - res = graph.build_llava(); - } break; + GGML_ABORT("missing cgraph builder"); } - return res; + + return builder->build(); } +// +// clip_model_loader +// + struct clip_model_loader { ggml_context_ptr ctx_meta; gguf_context_ptr ctx_gguf; @@ -2851,16 +1163,22 @@ struct clip_model_loader { } break; case PROJECTOR_TYPE_ULTRAVOX: case PROJECTOR_TYPE_QWEN2A: + case PROJECTOR_TYPE_GLMA: case PROJECTOR_TYPE_VOXTRAL: { bool require_stack = model.proj_type == PROJECTOR_TYPE_ULTRAVOX || - model.proj_type == PROJECTOR_TYPE_VOXTRAL; + model.proj_type == PROJECTOR_TYPE_VOXTRAL || + model.proj_type == PROJECTOR_TYPE_GLMA; get_u32(KEY_A_PROJ_STACK_FACTOR, hparams.proj_stack_factor, require_stack); - if (hparams.n_mel_bins != 128) { - throw std::runtime_error(string_format("%s: only 128 mel bins are supported for ultravox\n", __func__)); - } hparams.ffn_op = FFN_GELU_ERF; log_ffn_op = "gelu_erf"; // temporary solution for logging + + // audio preprocessing params + hparams.audio_chunk_len = 30; // in seconds + hparams.audio_sample_rate = 16000; + hparams.audio_n_fft = 400; + hparams.audio_window_len = 400; + hparams.audio_hop_len = 160; } break; default: break; @@ -2898,6 +1216,11 @@ struct clip_model_loader { LOG_INF("\n--- audio hparams ---\n"); LOG_INF("%s: n_mel_bins: %d\n", __func__, hparams.n_mel_bins); LOG_INF("%s: proj_stack_factor: %d\n", __func__, hparams.proj_stack_factor); + LOG_INF("%s: audio_chunk_len: %d\n", __func__, hparams.audio_chunk_len); + LOG_INF("%s: audio_sample_rate: %d\n", __func__, hparams.audio_sample_rate); + LOG_INF("%s: audio_n_fft: %d\n", __func__, hparams.audio_n_fft); + LOG_INF("%s: audio_window_len: %d\n", __func__, hparams.audio_window_len); + LOG_INF("%s: audio_hop_len: %d\n", __func__, hparams.audio_hop_len); } LOG_INF("\n"); LOG_INF("%s: model size: %.2f MiB\n", __func__, model_size / 1024.0 / 1024.0); @@ -3225,6 +1548,21 @@ struct clip_model_loader { model.mm_3_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "weight")); model.mm_3_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "bias")); } break; + case PROJECTOR_TYPE_GLMA: + { + model.conv1d_1_w = get_tensor(string_format(TN_CONV1D, 1, "weight")); + model.conv1d_1_b = get_tensor(string_format(TN_CONV1D, 1, "bias")); + model.conv1d_2_w = get_tensor(string_format(TN_CONV1D, 2, "weight")); + model.conv1d_2_b = get_tensor(string_format(TN_CONV1D, 2, "bias")); + model.mm_1_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "weight")); + model.mm_1_b = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "bias")); + model.mm_2_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "weight")); + model.mm_2_b = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "bias")); + model.mm_norm_pre_w = get_tensor(string_format(TN_MM_NORM_PRE, "weight")); + model.mm_norm_pre_b = get_tensor(string_format(TN_MM_NORM_PRE, "bias")); + model.mm_boi = get_tensor(string_format(TN_TOK_BOI, "weight")); + model.mm_eoi = get_tensor(string_format(TN_TOK_EOI, "weight")); + } break; case PROJECTOR_TYPE_LLAMA4: { model.mm_model_proj = get_tensor(TN_MM_PROJECTOR); @@ -3945,7 +2283,14 @@ struct llava_uhd { clip_image_size refined_size; // size of image right before slicing (must be multiple of slice size) clip_image_size grid_size; // grid_size.width * grid_size.height = number of slices std::vector slices; + + img_tool::resize_algo interpolation_overview = img_tool::RESIZE_ALGO_BILINEAR; + bool padding_overview = false; // if true, refine image will be padded to the grid size (e.g. llava-1.6) + std::array pad_color_overview = {0, 0, 0}; + + img_tool::resize_algo interpolation_refined = img_tool::RESIZE_ALGO_BICUBIC; bool padding_refined = false; // if true, refine image will be padded to the grid size (e.g. llava-1.6) + std::array pad_color_refined = {0, 0, 0}; }; static slice_instructions get_slice_instructions(struct clip_ctx * ctx, const clip_image_size & original_size) { @@ -3972,10 +2317,11 @@ struct llava_uhd { auto refine_size = llava_uhd::select_best_resolution( original_size, ctx->model.hparams.image_res_candidates); - res.overview_size = clip_image_size{slice_size, slice_size}; - res.refined_size = refine_size; - res.grid_size = clip_image_size{0, 0}; - res.padding_refined = true; + res.overview_size = clip_image_size{slice_size, slice_size}; + res.refined_size = refine_size; + res.grid_size = clip_image_size{0, 0}; + res.padding_refined = true; + res.interpolation_refined = img_tool::RESIZE_ALGO_BILINEAR; // preserve old behavior when padding LOG_DBG("%s: using pinpoints for slicing\n", __func__); LOG_DBG("%s: original size: %d x %d, overview size: %d x %d, refined size: %d x %d\n", @@ -4054,12 +2400,13 @@ struct llava_uhd { static std::vector slice_image(const clip_image_u8 * img, const slice_instructions & inst) { std::vector output; - img_tool::resize_algo interpolation = img_tool::RESIZE_ALGO_BILINEAR; // TODO: make it configurable // resize to overview size clip_image_u8_ptr resized_img(clip_image_u8_init()); - img_tool::resize(*img, *resized_img, inst.overview_size, interpolation); + img_tool::resize(*img, *resized_img, inst.overview_size, inst.interpolation_overview, + inst.padding_overview, inst.pad_color_overview); output.push_back(std::move(resized_img)); + if (inst.slices.empty()) { // no slices, just return the resized image return output; @@ -4067,13 +2414,8 @@ struct llava_uhd { // resize to refined size clip_image_u8_ptr refined_img(clip_image_u8_init()); - if (inst.padding_refined) { - img_tool::resize(*img, *refined_img, inst.refined_size, interpolation); - } else { - // only algo bicubic preserves the ratio; old models rely on this behavior - // TODO: do we need to support other algos here? - img_tool::resize(*img, *refined_img, inst.refined_size, img_tool::RESIZE_ALGO_BICUBIC, false); - } + img_tool::resize(*img, *refined_img, inst.refined_size, inst.interpolation_refined, + inst.padding_refined, inst.pad_color_refined); // create slices for (const auto & slice : inst.slices) { @@ -4606,6 +2948,16 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im n_patches /= 2; } } break; + case PROJECTOR_TYPE_GLMA: + { + n_patches = img->nx; + // whisper downscales input token by half after conv1d + n_patches /= 2; + // reshape by merge_factor + n_patches /= ctx->model.hparams.proj_stack_factor; + // for BOI and EOI token embeddings + n_patches += 2; + } break; case PROJECTOR_TYPE_COGVLM: { n_patches += 2; // for BOI and EOI token embeddings @@ -4941,6 +3293,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima case PROJECTOR_TYPE_IDEFICS3: case PROJECTOR_TYPE_INTERNVL: case PROJECTOR_TYPE_QWEN2A: + case PROJECTOR_TYPE_GLMA: case PROJECTOR_TYPE_ULTRAVOX: case PROJECTOR_TYPE_LFM2: case PROJECTOR_TYPE_VOXTRAL: @@ -5051,6 +3404,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { return ctx->model.mm_model_proj->ne[1]; case PROJECTOR_TYPE_QWEN2A: return ctx->model.mm_fc_w->ne[1]; + case PROJECTOR_TYPE_GLMA: + return ctx->model.mm_2_w->ne[1]; case PROJECTOR_TYPE_LFM2: case PROJECTOR_TYPE_KIMIVL: return ctx->model.mm_2_w->ne[1]; @@ -5097,6 +3452,7 @@ bool clip_has_audio_encoder(const struct clip_ctx * ctx) { bool clip_has_whisper_encoder(const struct clip_ctx * ctx) { return ctx->proj_type() == PROJECTOR_TYPE_ULTRAVOX || ctx->proj_type() == PROJECTOR_TYPE_QWEN2A + || ctx->proj_type() == PROJECTOR_TYPE_GLMA || ctx->proj_type() == PROJECTOR_TYPE_VOXTRAL; } @@ -5131,3 +3487,7 @@ void clip_image_f32_batch_add_mel(struct clip_image_f32_batch * batch, int n_mel batch->entries.push_back(clip_image_f32_ptr(audio)); batch->is_audio = true; } + +const clip_hparams * clip_get_hparams(const struct clip_ctx * ctx) { + return &ctx->model.hparams; +} diff --git a/tools/mtmd/clip.h b/tools/mtmd/clip.h index e8aeb2066c..5aae27a700 100644 --- a/tools/mtmd/clip.h +++ b/tools/mtmd/clip.h @@ -7,6 +7,8 @@ // !!! Internal header, to be used by mtmd only !!! +#define MTMD_INTERNAL_HEADER + struct clip_ctx; struct clip_image_size { diff --git a/tools/mtmd/models/cogvlm.cpp b/tools/mtmd/models/cogvlm.cpp new file mode 100644 index 0000000000..d5b739c687 --- /dev/null +++ b/tools/mtmd/models/cogvlm.cpp @@ -0,0 +1,98 @@ +#include "models.h" + +ggml_cgraph * clip_graph_cogvlm::build() { + GGML_ASSERT(model.class_embedding != nullptr); + GGML_ASSERT(model.position_embeddings != nullptr); + + const int n_pos = n_patches + 1; // +1 for [CLS] + + // build input and concatenate class embedding + ggml_tensor * inp = build_inp(); + inp = ggml_concat(ctx0, inp, model.class_embedding, 1); + + inp = ggml_add(ctx0, inp, model.position_embeddings); + cb(inp, "inp_pos", -1); + + ggml_tensor * inpL = inp; + + for (int il = 0; il < n_layer; il++) { + auto & layer = model.layers[il]; + ggml_tensor * cur = inpL; + + cur = ggml_mul_mat(ctx0, layer.qkv_w, cur); + + cur = ggml_add(ctx0, cur, layer.qkv_b); + + ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float), + cur->nb[1], 0); + ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float), + cur->nb[1], n_embd * sizeof(float)); + ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float), + cur->nb[1], 2 * n_embd * sizeof(float)); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(layer.o_w, layer.o_b, + Qcur, Kcur, Vcur, nullptr, kq_scale, il); + cb(cur, "attn_out", il); + + cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il); + cb(cur, "attn_post_norm", il); + + cur = ggml_add(ctx0, cur, inpL); + inpL = cur; + + cur = build_ffn(cur, + layer.ff_up_w, layer.ff_up_b, + layer.ff_gate_w, layer.ff_gate_b, + layer.ff_down_w, layer.ff_down_b, + hparams.ffn_op, il); + + cb(cur, "ffn_out", il); + + cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il); + cb(cur, "ffn_post_norm", il); + + cur = ggml_add(ctx0, cur, inpL); + cb(cur, "layer_out", il); + inpL = cur; + + } + + // remove CLS token (like build_llama4 does) + ggml_tensor * cur = ggml_view_2d(ctx0, inpL, + n_embd, n_patches, + ggml_row_size(inpL->type, n_embd), 0); + + // Multiply with mm_model_proj + cur = ggml_mul_mat(ctx0, model.mm_model_proj, cur); + + // Apply layernorm, weight, bias + cur = build_norm(cur, model.mm_post_fc_norm_w, model.mm_post_fc_norm_b, NORM_TYPE_NORMAL, 1e-5, -1); + + // Apply GELU + cur = ggml_gelu_inplace(ctx0, cur); + + // Branch 1: multiply with mm_h_to_4h_w + ggml_tensor * h_to_4h = ggml_mul_mat(ctx0, model.mm_h_to_4h_w, cur); + + // Branch 2: multiply with mm_gate_w + ggml_tensor * gate = ggml_mul_mat(ctx0, model.mm_gate_w, cur); + + // Apply silu + gate = ggml_swiglu_split(ctx0, gate, h_to_4h); + + // Apply mm_4h_to_h_w + cur = ggml_mul_mat(ctx0, model.mm_4h_to_h_w, gate); + + // Concatenate with boi and eoi + cur = ggml_concat(ctx0, model.mm_boi, cur, 1); + cur = ggml_concat(ctx0, cur, model.mm_eoi, 1); + + // build the graph + ggml_build_forward_expand(gf, cur); + + return gf; +} diff --git a/tools/mtmd/models/internvl.cpp b/tools/mtmd/models/internvl.cpp new file mode 100644 index 0000000000..9aded3b97c --- /dev/null +++ b/tools/mtmd/models/internvl.cpp @@ -0,0 +1,69 @@ +#include "models.h" + +ggml_cgraph * clip_graph_internvl::build() { + GGML_ASSERT(model.class_embedding != nullptr); + GGML_ASSERT(model.position_embeddings != nullptr); + + const int n_pos = n_patches + 1; + ggml_tensor * inp = build_inp(); + + // add CLS token + inp = ggml_concat(ctx0, inp, model.class_embedding, 1); + + // The larger models use a different ViT, which uses RMS norm instead of layer norm + // ref: https://github.com/ggml-org/llama.cpp/pull/13443#issuecomment-2869786188 + norm_type norm_t = (hparams.n_embd == 3200 && hparams.n_layer == 45) + ? NORM_TYPE_RMS // 6B ViT (Used by InternVL 2.5/3 - 26B, 38B, 78B) + : NORM_TYPE_NORMAL; // 300M ViT (Used by all smaller InternVL models) + + ggml_tensor * cur = build_vit( + inp, n_pos, + norm_t, + hparams.ffn_op, + model.position_embeddings, + nullptr); + + // remove CLS token + cur = ggml_view_2d(ctx0, cur, + n_embd, n_patches, + ggml_row_size(cur->type, n_embd), 0); + + // pixel shuffle + { + const int scale_factor = model.hparams.n_merge; + const int bsz = 1; // batch size, always 1 for now since we don't support batching + const int height = n_patches_y; + const int width = n_patches_x; + GGML_ASSERT(scale_factor > 0); + cur = ggml_reshape_4d(ctx0, cur, n_embd * scale_factor, height / scale_factor, width, bsz); + cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); + cur = ggml_cont_4d(ctx0, cur, + n_embd * scale_factor * scale_factor, + height / scale_factor, + width / scale_factor, + bsz); + cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); + // flatten to 2D + cur = ggml_cont_2d(ctx0, cur, + n_embd * scale_factor * scale_factor, + cur->ne[1] * cur->ne[2]); + } + + // projector (always using GELU activation) + { + // projector LayerNorm uses pytorch's default eps = 1e-5 + // ref: https://huggingface.co/OpenGVLab/InternVL3-8B-Instruct/blob/a34d3e4e129a5856abfd6aa6de79776484caa14e/modeling_internvl_chat.py#L79 + cur = build_norm(cur, model.mm_0_w, model.mm_0_b, NORM_TYPE_NORMAL, 1e-5, -1); + cur = build_ffn(cur, + model.mm_1_w, model.mm_1_b, + nullptr, nullptr, + model.mm_3_w, model.mm_3_b, + FFN_GELU, + -1); + } + + // build the graph + ggml_build_forward_expand(gf, cur); + + return gf; +} diff --git a/tools/mtmd/models/kimivl.cpp b/tools/mtmd/models/kimivl.cpp new file mode 100644 index 0000000000..0a06f5090e --- /dev/null +++ b/tools/mtmd/models/kimivl.cpp @@ -0,0 +1,63 @@ +#include "models.h" + +ggml_cgraph * clip_graph_kimivl::build() { + // 2D input positions + ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(pos_h, "pos_h"); + ggml_set_input(pos_h); + + ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(pos_w, "pos_w"); + ggml_set_input(pos_w); + + ggml_tensor * learned_pos_embd = resize_position_embeddings(); + + // build ViT with 2D position embeddings + auto add_pos = [&](ggml_tensor * cur, const clip_layer &) { + // first half is X axis and second half is Y axis + return build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false); + }; + + ggml_tensor * inp = build_inp(); + ggml_tensor * cur = build_vit( + inp, n_patches, + NORM_TYPE_NORMAL, + hparams.ffn_op, + learned_pos_embd, + add_pos); + + cb(cur, "vit_out", -1); + + { + // patch_merger + const int scale_factor = model.hparams.n_merge; + cur = build_patch_merge_permute(cur, scale_factor); + + // projection norm + int proj_inp_dim = cur->ne[0]; + cur = ggml_view_2d(ctx0, cur, + n_embd, cur->ne[1] * scale_factor * scale_factor, + ggml_row_size(cur->type, n_embd), 0); + cur = ggml_norm(ctx0, cur, 1e-5); // default nn.LayerNorm + cur = ggml_mul(ctx0, cur, model.mm_input_norm_w); + cur = ggml_add(ctx0, cur, model.mm_input_norm_b); + cur = ggml_view_2d(ctx0, cur, + proj_inp_dim, cur->ne[1] / scale_factor / scale_factor, + ggml_row_size(cur->type, proj_inp_dim), 0); + cb(cur, "proj_inp_normed", -1); + + // projection mlp + cur = build_ffn(cur, + model.mm_1_w, model.mm_1_b, + nullptr, nullptr, + model.mm_2_w, model.mm_2_b, + FFN_GELU, + -1); + cb(cur, "proj_out", -1); + } + + // build the graph + ggml_build_forward_expand(gf, cur); + + return gf; +} diff --git a/tools/mtmd/models/llama4.cpp b/tools/mtmd/models/llama4.cpp new file mode 100644 index 0000000000..30d1df5bcd --- /dev/null +++ b/tools/mtmd/models/llama4.cpp @@ -0,0 +1,96 @@ +#include "models.h" + +ggml_cgraph * clip_graph_llama4::build() { + GGML_ASSERT(model.class_embedding != nullptr); + GGML_ASSERT(model.position_embeddings != nullptr); + + const int n_pos = n_patches + 1; // +1 for [CLS] + + // 2D input positions + ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); + ggml_set_name(pos_h, "pos_h"); + ggml_set_input(pos_h); + + ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); + ggml_set_name(pos_w, "pos_w"); + ggml_set_input(pos_w); + + ggml_tensor * inp = build_inp_raw(); + + // Llama4UnfoldConvolution + { + ggml_tensor * kernel = ggml_reshape_4d(ctx0, model.patch_embeddings_0, + patch_size, patch_size, 3, n_embd); + inp = ggml_im2col(ctx0, kernel, inp, patch_size, patch_size, 0, 0, 1, 1, true, inp->type); + inp = ggml_mul_mat(ctx0, model.patch_embeddings_0, inp); + inp = ggml_reshape_2d(ctx0, inp, n_embd, n_patches); + cb(inp, "patch_conv", -1); + } + + // add CLS token + inp = ggml_concat(ctx0, inp, model.class_embedding, 1); + + // build ViT with 2D position embeddings + auto add_pos = [&](ggml_tensor * cur, const clip_layer &) { + // first half is X axis and second half is Y axis + // ref: https://github.com/huggingface/transformers/blob/40a493c7ed4f19f08eadb0639cf26d49bfa5e180/src/transformers/models/llama4/modeling_llama4.py#L1312 + // ref: https://github.com/Blaizzy/mlx-vlm/blob/a57156aa87b33cca6e5ee6cfc14dd4ef8f611be6/mlx_vlm/models/llama4/vision.py#L441 + return build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false); + }; + ggml_tensor * cur = build_vit( + inp, n_pos, + NORM_TYPE_NORMAL, + hparams.ffn_op, + model.position_embeddings, + add_pos); + + // remove CLS token + cur = ggml_view_2d(ctx0, cur, + n_embd, n_patches, + ggml_row_size(cur->type, n_embd), 0); + + // pixel shuffle + // based on Llama4VisionPixelShuffleMLP + // https://github.com/huggingface/transformers/blob/2932f318a20d9e54cc7aea052e040164d85de7d6/src/transformers/models/llama4/modeling_llama4.py#L1151 + { + const int scale_factor = model.hparams.n_merge; + const int bsz = 1; // batch size, always 1 for now since we don't support batching + GGML_ASSERT(scale_factor > 0); + GGML_ASSERT(n_patches_x == n_patches_y); // llama4 only supports square images + cur = ggml_reshape_4d(ctx0, cur, + n_embd * scale_factor, + n_patches_x / scale_factor, + n_patches_y, + bsz); + cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); + cur = ggml_cont_4d(ctx0, cur, + n_embd * scale_factor * scale_factor, + n_patches_x / scale_factor, + n_patches_y / scale_factor, + bsz); + //cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); + // flatten to 2D + cur = ggml_cont_2d(ctx0, cur, + n_embd * scale_factor * scale_factor, + n_patches / scale_factor / scale_factor); + cb(cur, "pixel_shuffle", -1); + } + + // based on Llama4VisionMLP2 (always uses GELU activation, no bias) + { + cur = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w, cur); + cur = ggml_gelu(ctx0, cur); + cur = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, cur); + cur = ggml_gelu(ctx0, cur); + cb(cur, "adapter_mlp", -1); + } + + // Llama4MultiModalProjector + cur = ggml_mul_mat(ctx0, model.mm_model_proj, cur); + cb(cur, "projected", -1); + + // build the graph + ggml_build_forward_expand(gf, cur); + + return gf; +} diff --git a/tools/mtmd/models/llava.cpp b/tools/mtmd/models/llava.cpp new file mode 100644 index 0000000000..0bfb5f05f6 --- /dev/null +++ b/tools/mtmd/models/llava.cpp @@ -0,0 +1,374 @@ +#include "models.h" + +// this graph is used by llava, granite and glm +// due to having embedding_stack (used by granite), we cannot reuse build_vit +ggml_cgraph * clip_graph_llava::build() { + const int batch_size = 1; + const int n_pos = n_patches + (model.class_embedding ? 1 : 0); + + GGML_ASSERT(n_patches_x == n_patches_y && "only square images supported"); + + // Calculate the deepest feature layer based on hparams and projector type + int max_feature_layer = n_layer; + { + // Get the index of the second to last layer; this is the default for models that have a llava projector + int il_last = hparams.n_layer - 1; + int deepest_feature_layer = -1; + + if (proj_type == PROJECTOR_TYPE_MINICPMV || proj_type == PROJECTOR_TYPE_GLM_EDGE) { + il_last += 1; + } + + // If we set explicit vision feature layers, only go up to the deepest one + // NOTE: only used by granite-vision models for now + for (const auto & feature_layer : hparams.vision_feature_layer) { + if (feature_layer > deepest_feature_layer) { + deepest_feature_layer = feature_layer; + } + } + max_feature_layer = deepest_feature_layer < 0 ? il_last : deepest_feature_layer; + } + + ggml_tensor * inp = build_inp(); + + // concat class_embeddings and patch_embeddings + if (model.class_embedding) { + inp = ggml_concat(ctx0, inp, model.class_embedding, 1); + } + + ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); + ggml_set_name(positions, "positions"); + ggml_set_input(positions); + + inp = ggml_add(ctx0, inp, ggml_get_rows(ctx0, model.position_embeddings, positions)); + + ggml_tensor * inpL = inp; + + // pre-layernorm + if (model.pre_ln_w) { + inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, NORM_TYPE_NORMAL, eps, -1); + cb(inpL, "pre_ln", -1); + } + + std::vector embedding_stack; + const auto & vision_feature_layer = hparams.vision_feature_layer; + + // loop over layers + for (int il = 0; il < max_feature_layer; il++) { + auto & layer = model.layers[il]; + ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states + + // If this is an embedding feature layer, save the output. + // NOTE: 0 index here refers to the input to the encoder. + if (vision_feature_layer.find(il) != vision_feature_layer.end()) { + embedding_stack.push_back(cur); + } + + // layernorm1 + cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il); + cb(cur, "layer_inp_normed", il); + + // self-attention + { + ggml_tensor * Qcur = ggml_mul_mat(ctx0, layer.q_w, cur); + if (layer.q_b) { + Qcur = ggml_add(ctx0, Qcur, layer.q_b); + } + + ggml_tensor * Kcur = ggml_mul_mat(ctx0, layer.k_w, cur); + if (layer.k_b) { + Kcur = ggml_add(ctx0, Kcur, layer.k_b); + } + + ggml_tensor * Vcur = ggml_mul_mat(ctx0, layer.v_w, cur); + if (layer.v_b) { + Vcur = ggml_add(ctx0, Vcur, layer.v_b); + } + + Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos); + Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos); + Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(layer.o_w, layer.o_b, + Qcur, Kcur, Vcur, nullptr, kq_scale, il); + cb(cur, "attn_out", il); + } + + // re-add the layer input, e.g., residual + cur = ggml_add(ctx0, cur, inpL); + + inpL = cur; // inpL = residual, cur = hidden_states + + cb(cur, "ffn_inp", il); + + // layernorm2 + cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il); + cb(cur, "ffn_inp_normed", il); + + // ffn + cur = build_ffn(cur, + layer.ff_up_w, layer.ff_up_b, + layer.ff_gate_w, layer.ff_gate_b, + layer.ff_down_w, layer.ff_down_b, + hparams.ffn_op, il); + + cb(cur, "ffn_out", il); + + // residual 2 + cur = ggml_add(ctx0, inpL, cur); + cb(cur, "layer_out", il); + + inpL = cur; + } + + // post-layernorm + if (model.post_ln_w) { + inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, NORM_TYPE_NORMAL, eps, -1); + } + + ggml_tensor * embeddings = inpL; + + // process vision feature layers (used by granite) + { + // final layer is a vision feature layer + if (vision_feature_layer.find(max_feature_layer) != vision_feature_layer.end()) { + embedding_stack.push_back(inpL); + } + + // If feature layers are explicitly set, stack them (if we have multiple) + if (!embedding_stack.empty()) { + embeddings = embedding_stack[0]; + for (size_t i = 1; i < embedding_stack.size(); i++) { + embeddings = ggml_concat(ctx0, embeddings, embedding_stack[i], 0); + } + } + } + + // llava projector (also used by granite) + if (hparams.has_llava_projector) { + embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]); + + ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(patches, "patches"); + ggml_set_input(patches); + + // shape [1, 576, 1024] + // ne is whcn, ne = [1024, 576, 1, 1] + embeddings = ggml_get_rows(ctx0, embeddings, patches); + + // print_tensor_info(embeddings, "embeddings"); + + // llava projector + if (proj_type == PROJECTOR_TYPE_MLP) { + embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); + + embeddings = ggml_gelu(ctx0, embeddings); + if (model.mm_2_w) { + embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_2_b); + } + } + else if (proj_type == PROJECTOR_TYPE_MLP_NORM) { + embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); + // ggml_tensor_printf(embeddings, "mm_0_w",0,true,false); + // First LayerNorm + embeddings = ggml_norm(ctx0, embeddings, eps); + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_1_w), + model.mm_1_b); + + // GELU activation + embeddings = ggml_gelu(ctx0, embeddings); + + // Second linear layer + embeddings = ggml_mul_mat(ctx0, model.mm_3_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_3_b); + + // Second LayerNorm + embeddings = ggml_norm(ctx0, embeddings, eps); + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_4_w), + model.mm_4_b); + } + else if (proj_type == PROJECTOR_TYPE_LDP) { + // MobileVLM projector + int n_patch = 24; + ggml_tensor * mlp_1 = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w, embeddings); + mlp_1 = ggml_add(ctx0, mlp_1, model.mm_model_mlp_1_b); + mlp_1 = ggml_gelu(ctx0, mlp_1); + ggml_tensor * mlp_3 = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, mlp_1); + mlp_3 = ggml_add(ctx0, mlp_3, model.mm_model_mlp_3_b); + // mlp_3 shape = [1, 576, 2048], ne = [2048, 576, 1, 1] + + // block 1 + ggml_tensor * block_1 = nullptr; + { + // transpose from [1, 576, 2048] --> [1, 2048, 576] --> [1, 2048, 24, 24] + mlp_3 = ggml_permute(ctx0, mlp_3, 1, 0, 2, 3); + mlp_3 = ggml_cont_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]); + // stride = 1, padding = 1, bias is nullptr + block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1); + + // layer norm + // // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3)); + // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1] + block_1 = ggml_norm(ctx0, block_1, eps); + block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_0_1_w), model.mm_model_block_1_block_0_1_b); + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3)); + + // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] + // hardswish + ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1); + + block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0); + // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1] + // pointwise conv + block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]); + block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc1_w, block_1); + block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc1_b); + block_1 = ggml_relu(ctx0, block_1); + block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc2_w, block_1); + block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc2_b); + block_1 = ggml_hardsigmoid(ctx0, block_1); + // block_1_hw shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1], block_1 shape = [1, 2048], ne = [2048, 1, 1, 1] + block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]); + block_1 = ggml_mul(ctx0, block_1_hw, block_1); + + int w = block_1->ne[0], h = block_1->ne[1]; + block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]); + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3)); + + // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1] + block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_2_0_w, block_1); + block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]); + + // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1] + block_1 = ggml_norm(ctx0, block_1, eps); + block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_2_1_w), model.mm_model_block_1_block_2_1_b); + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3)); + // block1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] + // residual + block_1 = ggml_add(ctx0, mlp_3, block_1); + } + + // block_2 + { + // stride = 2 + block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1); + + // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1] + // layer norm + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3)); + // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1] + block_1 = ggml_norm(ctx0, block_1, eps); + block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_0_1_w), model.mm_model_block_2_block_0_1_b); + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3)); + // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1] + // hardswish + ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1); + + // not sure the parameters is right for globalAvgPooling + block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0); + // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1] + // pointwise conv + block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]); + block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc1_w, block_1); + block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc1_b); + block_1 = ggml_relu(ctx0, block_1); + block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc2_w, block_1); + block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc2_b); + block_1 = ggml_hardsigmoid(ctx0, block_1); + + // block_1_hw shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1], block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1] + block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]); + block_1 = ggml_mul(ctx0, block_1_hw, block_1); + + int w = block_1->ne[0], h = block_1->ne[1]; + block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]); + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3)); + // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1] + block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_2_0_w, block_1); + block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]); + + + // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1] + block_1 = ggml_norm(ctx0, block_1, eps); + block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_2_1_w), model.mm_model_block_2_block_2_1_b); + block_1 = ggml_reshape_3d(ctx0, block_1, block_1->ne[0], block_1->ne[1] * block_1->ne[2], block_1->ne[3]); + // block_1 shape = [1, 144, 2048], ne = [2048, 144, 1] + } + embeddings = block_1; + } + else if (proj_type == PROJECTOR_TYPE_LDPV2) + { + int n_patch = 24; + ggml_tensor * mlp_0 = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); + mlp_0 = ggml_add(ctx0, mlp_0, model.mm_model_mlp_0_b); + mlp_0 = ggml_gelu(ctx0, mlp_0); + ggml_tensor * mlp_2 = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, mlp_0); + mlp_2 = ggml_add(ctx0, mlp_2, model.mm_model_mlp_2_b); + // mlp_2 ne = [2048, 576, 1, 1] + // // AVG Pool Layer 2*2, strides = 2 + mlp_2 = ggml_permute(ctx0, mlp_2, 1, 0, 2, 3); + // mlp_2 ne = [576, 2048, 1, 1] + mlp_2 = ggml_cont_4d(ctx0, mlp_2, n_patch, n_patch, mlp_2->ne[1], mlp_2->ne[2]); + // mlp_2 ne [24, 24, 2048, 1] + mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0); + // weight ne = [3, 3, 2048, 1] + ggml_tensor * peg_0 = ggml_conv_2d_dw(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1); + peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3)); + peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b); + mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3)); + peg_0 = ggml_add(ctx0, peg_0, mlp_2); + peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]); + embeddings = peg_0; + } + else { + GGML_ABORT("fatal error"); + } + } + + // glm projector + else if (proj_type == PROJECTOR_TYPE_GLM_EDGE) { + size_t gridsz = (size_t)sqrt(embeddings->ne[1]); + embeddings = ggml_permute(ctx0,embeddings,1,0,2,3); + embeddings = ggml_cont_3d(ctx0, embeddings, gridsz, gridsz, embeddings->ne[1]); + embeddings = ggml_conv_2d(ctx0, model.mm_model_adapter_conv_w, embeddings, 2, 2, 0, 0, 1, 1); + embeddings = ggml_reshape_3d(ctx0, embeddings,embeddings->ne[0]*embeddings->ne[1] , embeddings->ne[2], batch_size); + embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings, 1, 0, 2, 3)); + embeddings = ggml_add(ctx0, embeddings, model.mm_model_adapter_conv_b); + // GLU + { + embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); + embeddings = ggml_norm(ctx0, embeddings, eps); + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_q_w), model.mm_model_ln_q_b); + embeddings = ggml_gelu_inplace(ctx0, embeddings); + ggml_tensor * x = embeddings; + embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, embeddings); + x = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w,x); + embeddings = ggml_swiglu_split(ctx0, embeddings, x); + embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, embeddings); + } + // arrangement of BOI/EOI token embeddings + // note: these embeddings are not present in text model, hence we cannot process them as text tokens + // see: https://huggingface.co/THUDM/glm-edge-v-2b/blob/main/siglip.py#L53 + { + embeddings = ggml_concat(ctx0, model.mm_boi, embeddings, 1); // BOI + embeddings = ggml_concat(ctx0, embeddings, model.mm_eoi, 1); // EOI + } + } + + else { + GGML_ABORT("llava: unknown projector type"); + } + + // build the graph + ggml_build_forward_expand(gf, embeddings); + + return gf; +} diff --git a/tools/mtmd/models/minicpmv.cpp b/tools/mtmd/models/minicpmv.cpp new file mode 100644 index 0000000000..3594ea29fa --- /dev/null +++ b/tools/mtmd/models/minicpmv.cpp @@ -0,0 +1,114 @@ +#include "models.h" + +ggml_cgraph * clip_graph_minicpmv::build() { + GGML_ASSERT(model.class_embedding == nullptr); + const int n_pos = n_patches; + const int n_embd_proj = n_mmproj_embd; + + // position embeddings for the projector (not for ViT) + // see: https://huggingface.co/openbmb/MiniCPM-o-2_6/blob/main/resampler.py#L70 + // base frequency omega + ggml_tensor * omega = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_embd_proj / 4); + ggml_set_name(omega, "omega"); + ggml_set_input(omega); + + // 2D input positions (using float for sinusoidal embeddings) + ggml_tensor * pos_h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_pos); + ggml_set_name(pos_h, "pos_h"); + ggml_set_input(pos_h); + ggml_tensor * pos_w = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_pos); + ggml_set_name(pos_w, "pos_w"); + ggml_set_input(pos_w); + + // for selecting learned pos embd, used by ViT + struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); + ggml_set_name(positions, "positions"); + ggml_set_input(positions); + + ggml_tensor * learned_pos_embd = ggml_get_rows(ctx0, model.position_embeddings, positions); + + ggml_tensor * inp = build_inp(); + ggml_tensor * embeddings = build_vit( + inp, n_pos, + NORM_TYPE_NORMAL, + hparams.ffn_op, + learned_pos_embd, + nullptr); + + // resampler projector (it is just another transformer) + + ggml_tensor * q = model.mm_model_query; + ggml_tensor * v = ggml_mul_mat(ctx0, model.mm_model_kv_proj, embeddings); + + // norm + q = build_norm(q, model.mm_model_ln_q_w, model.mm_model_ln_q_b, NORM_TYPE_NORMAL, eps, -1); + v = build_norm(v, model.mm_model_ln_kv_w, model.mm_model_ln_kv_b, NORM_TYPE_NORMAL, eps, -1); + + // calculate sinusoidal pos embd + ggml_tensor * pos_embed = nullptr; + { + // outer product + ggml_tensor * omega_b = ggml_repeat_4d(ctx0, omega, omega->ne[0], n_pos, 1, 1); // n_pos rows + ggml_tensor * theta_x = ggml_mul(ctx0, omega_b, pos_w); + ggml_tensor * theta_y = ggml_mul(ctx0, omega_b, pos_h); + // sin and cos + ggml_tensor * pos_embd_x = ggml_concat( + ctx0, + ggml_sin(ctx0, theta_x), + ggml_cos(ctx0, theta_x), + 0 // concat on first dim + ); + ggml_tensor * pos_embd_y = ggml_concat( + ctx0, + ggml_sin(ctx0, theta_y), + ggml_cos(ctx0, theta_y), + 0 // concat on first dim + ); + pos_embed = ggml_concat(ctx0, pos_embd_x, pos_embd_y, 0); + } + + // k = v + pos_embed + ggml_tensor * k = ggml_add(ctx0, v, pos_embed); + + // attention + { + const int d_head = 128; + int n_head = n_embd_proj/d_head; + // Use actual config value if available, otherwise fall back to hardcoded values + int num_query = hparams.minicpmv_query_num; + ggml_tensor * Q = ggml_add(ctx0, + ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q), + model.mm_model_attn_q_b); + ggml_tensor * K = ggml_add(ctx0, + ggml_mul_mat(ctx0, model.mm_model_attn_k_w, k), + model.mm_model_attn_k_b); + ggml_tensor * V = ggml_add(ctx0, + ggml_mul_mat(ctx0, model.mm_model_attn_v_w, v), + model.mm_model_attn_v_b); + + Q = ggml_reshape_3d(ctx0, Q, d_head, n_head, num_query); + K = ggml_reshape_3d(ctx0, K, d_head, n_head, n_pos); + V = ggml_reshape_3d(ctx0, V, d_head, n_head, n_pos); + + cb(Q, "resampler_Q", -1); + cb(K, "resampler_K", -1); + cb(V, "resampler_V", -1); + + float resampler_kq_scale = 1.0f/ sqrtf(float(d_head)); + embeddings = build_attn( + model.mm_model_attn_o_w, + model.mm_model_attn_o_b, + Q, K, V, nullptr, resampler_kq_scale, -1); + cb(embeddings, "resampler_attn_out", -1); + } + // layernorm + embeddings = build_norm(embeddings, model.mm_model_ln_post_w, model.mm_model_ln_post_b, NORM_TYPE_NORMAL, eps, -1); + + // projection + embeddings = ggml_mul_mat(ctx0, model.mm_model_proj, embeddings); + + // build the graph + ggml_build_forward_expand(gf, embeddings); + + return gf; +} diff --git a/tools/mtmd/models/models.h b/tools/mtmd/models/models.h new file mode 100644 index 0000000000..4b35da259c --- /dev/null +++ b/tools/mtmd/models/models.h @@ -0,0 +1,58 @@ +#pragma once + +#include "../clip-graph.h" + +struct clip_graph_siglip : clip_graph { + clip_graph_siglip(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_pixtral : clip_graph { + clip_graph_pixtral(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_qwen2vl : clip_graph { + clip_graph_qwen2vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_qwen3vl : clip_graph { + clip_graph_qwen3vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_minicpmv : clip_graph { + clip_graph_minicpmv(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_internvl : clip_graph { + clip_graph_internvl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_llama4 : clip_graph { + clip_graph_llama4(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_kimivl : clip_graph { + clip_graph_kimivl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_cogvlm : clip_graph { + clip_graph_cogvlm(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_llava : clip_graph { + clip_graph_llava(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_whisper_enc : clip_graph { + clip_graph_whisper_enc(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; diff --git a/tools/mtmd/models/pixtral.cpp b/tools/mtmd/models/pixtral.cpp new file mode 100644 index 0000000000..a849210b53 --- /dev/null +++ b/tools/mtmd/models/pixtral.cpp @@ -0,0 +1,86 @@ +#include "models.h" + +ggml_cgraph * clip_graph_pixtral::build() { + const int n_merge = hparams.n_merge; + + // 2D input positions + ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(pos_h, "pos_h"); + ggml_set_input(pos_h); + + ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(pos_w, "pos_w"); + ggml_set_input(pos_w); + + auto add_pos = [&](ggml_tensor * cur, const clip_layer &) { + return build_rope_2d(ctx0, cur, pos_h, pos_w, hparams.rope_theta, true); + }; + + ggml_tensor * inp = build_inp(); + ggml_tensor * cur = build_vit( + inp, n_patches, + NORM_TYPE_RMS, + hparams.ffn_op, + nullptr, // no learned pos embd + add_pos); + + // mistral small 3.1 patch merger + // ref: https://github.com/huggingface/transformers/blob/7a3e208892c06a5e278144eaf38c8599a42f53e7/src/transformers/models/mistral3/modeling_mistral3.py#L67 + if (model.mm_patch_merger_w) { + GGML_ASSERT(hparams.n_merge > 0); + + cur = ggml_mul(ctx0, ggml_rms_norm(ctx0, cur, eps), model.mm_input_norm_w); + + // reshape image tokens to 2D grid + cur = ggml_reshape_3d(ctx0, cur, n_embd, n_patches_x, n_patches_y); + cur = ggml_permute(ctx0, cur, 2, 0, 1, 3); // [x, y, n_embd] + cur = ggml_cont(ctx0, cur); + + // torch.nn.functional.unfold is just an im2col under the hood + // we just need a dummy kernel to make it work + ggml_tensor * kernel = ggml_view_3d(ctx0, cur, n_merge, n_merge, cur->ne[2], 0, 0, 0); + cur = ggml_im2col(ctx0, kernel, cur, n_merge, n_merge, 0, 0, 1, 1, true, inp->type); + + // project to n_embd + cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]); + cur = ggml_mul_mat(ctx0, model.mm_patch_merger_w, cur); + } + + // LlavaMultiModalProjector (always using GELU activation) + { + cur = build_ffn(cur, + model.mm_1_w, model.mm_1_b, + nullptr, nullptr, + model.mm_2_w, model.mm_2_b, + FFN_GELU, + -1); + } + + // arrangement of the [IMG_BREAK] token + if (model.token_embd_img_break) { + // not efficient, but works + // the trick is to view the embeddings as a 3D tensor with shape [n_embd, n_patches_per_row, n_rows] + // and then concatenate the [IMG_BREAK] token to the end of each row, aka n_patches_per_row dimension + // after the concatenation, we have a tensor with shape [n_embd, n_patches_per_row + 1, n_rows] + + const int p_y = n_merge > 0 ? n_patches_y / n_merge : n_patches_y; + const int p_x = n_merge > 0 ? n_patches_x / n_merge : n_patches_x; + const int p_total = p_x * p_y; + const int n_embd_text = cur->ne[0]; + const int n_tokens_output = p_total + p_y - 1; // one [IMG_BREAK] per row, except the last row + + ggml_tensor * tmp = ggml_reshape_3d(ctx0, cur, n_embd_text, p_x, p_y); + ggml_tensor * tok = ggml_new_tensor_3d(ctx0, tmp->type, n_embd_text, 1, p_y); + tok = ggml_scale(ctx0, tok, 0.0); // clear the tensor + tok = ggml_add(ctx0, tok, model.token_embd_img_break); + tmp = ggml_concat(ctx0, tmp, tok, 1); + cur = ggml_view_2d(ctx0, tmp, + n_embd_text, n_tokens_output, + ggml_row_size(tmp->type, n_embd_text), 0); + } + + // build the graph + ggml_build_forward_expand(gf, cur); + + return gf; +} diff --git a/tools/mtmd/models/qwen2vl.cpp b/tools/mtmd/models/qwen2vl.cpp new file mode 100644 index 0000000000..85f158bb1c --- /dev/null +++ b/tools/mtmd/models/qwen2vl.cpp @@ -0,0 +1,183 @@ +#include "models.h" + +ggml_cgraph * clip_graph_qwen2vl::build() { + GGML_ASSERT(model.patch_bias == nullptr); + GGML_ASSERT(model.class_embedding == nullptr); + + const int batch_size = 1; + const bool use_window_attn = hparams.n_wa_pattern > 0; + const int n_wa_pattern = hparams.n_wa_pattern; + const int n_pos = n_patches; + const int num_position_ids = n_pos * 4; // m-rope requires 4 dim per position + + norm_type norm_t = proj_type == PROJECTOR_TYPE_QWEN25VL + ? NORM_TYPE_RMS // qwen 2.5 vl + : NORM_TYPE_NORMAL; // qwen 2 vl + + int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4}; + + ggml_tensor * inp_raw = build_inp_raw(); + ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); + + GGML_ASSERT(img.nx % (patch_size * 2) == 0); + GGML_ASSERT(img.ny % (patch_size * 2) == 0); + + // second conv dimension + { + auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1); + inp = ggml_add(ctx0, inp, inp_1); + + inp = ggml_permute(ctx0, inp, 1, 2, 0, 3); // [w, h, c, b] -> [c, w, h, b] + inp = ggml_cont_4d( + ctx0, inp, + n_embd * 2, n_patches_x / 2, n_patches_y, batch_size); + inp = ggml_reshape_4d( + ctx0, inp, + n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2)); + inp = ggml_permute(ctx0, inp, 0, 2, 1, 3); + inp = ggml_cont_3d( + ctx0, inp, + n_embd, n_patches_x * n_patches_y, batch_size); + } + + ggml_tensor * inpL = inp; + ggml_tensor * window_mask = nullptr; + ggml_tensor * window_idx = nullptr; + ggml_tensor * inv_window_idx = nullptr; + + ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids); + ggml_set_name(positions, "positions"); + ggml_set_input(positions); + + // pre-layernorm + if (model.pre_ln_w) { + inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1); + } + + if (use_window_attn) { + // handle window attention inputs + inv_window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / 4); + ggml_set_name(inv_window_idx, "inv_window_idx"); + ggml_set_input(inv_window_idx); + // mask for window attention + window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos); + ggml_set_name(window_mask, "window_mask"); + ggml_set_input(window_mask); + + // if flash attn is used, we need to pad the mask and cast to f16 + if (flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) { + window_mask = ggml_cast(ctx0, window_mask, GGML_TYPE_F16); + } + + // inpL shape: [n_embd, n_patches_x * n_patches_y, batch_size] + GGML_ASSERT(batch_size == 1); + inpL = ggml_reshape_2d(ctx0, inpL, n_embd * 4, n_patches_x * n_patches_y * batch_size / 4); + inpL = ggml_get_rows(ctx0, inpL, inv_window_idx); + inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_patches_x * n_patches_y, batch_size); + } + + // loop over layers + for (int il = 0; il < n_layer; il++) { + const auto & layer = model.layers[il]; + const bool full_attn = use_window_attn ? (il + 1) % n_wa_pattern == 0 : true; + + ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states + + // layernorm1 + cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il); + cb(cur, "ln1", il); + + // self-attention + { + ggml_tensor * Qcur = ggml_add(ctx0, + ggml_mul_mat(ctx0, layer.q_w, cur), layer.q_b); + ggml_tensor * Kcur = ggml_add(ctx0, + ggml_mul_mat(ctx0, layer.k_w, cur), layer.k_b); + ggml_tensor * Vcur = ggml_add(ctx0, + ggml_mul_mat(ctx0, layer.v_w, cur), layer.v_b); + + Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_patches); + Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_patches); + Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_patches); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + // apply M-RoPE + Qcur = ggml_rope_multi( + ctx0, Qcur, positions, nullptr, + d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); + Kcur = ggml_rope_multi( + ctx0, Kcur, positions, nullptr, + d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); + + cb(Qcur, "Qcur_rope", il); + cb(Kcur, "Kcur_rope", il); + + ggml_tensor * attn_mask = full_attn ? nullptr : window_mask; + + cur = build_attn(layer.o_w, layer.o_b, + Qcur, Kcur, Vcur, attn_mask, kq_scale, il); + cb(cur, "attn_out", il); + } + + // re-add the layer input, e.g., residual + cur = ggml_add(ctx0, cur, inpL); + + inpL = cur; // inpL = residual, cur = hidden_states + + cb(cur, "ffn_inp", il); + + // layernorm2 + cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il); + cb(cur, "ffn_inp_normed", il); + + // ffn + cur = build_ffn(cur, + layer.ff_up_w, layer.ff_up_b, + layer.ff_gate_w, layer.ff_gate_b, + layer.ff_down_w, layer.ff_down_b, + hparams.ffn_op, il); + + cb(cur, "ffn_out", il); + + // residual 2 + cur = ggml_add(ctx0, inpL, cur); + cb(cur, "layer_out", il); + + inpL = cur; + } + + // post-layernorm + if (model.post_ln_w) { + inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, n_layer); + } + + // multimodal projection + ggml_tensor * embeddings = inpL; + embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, n_pos / 4, batch_size); + embeddings = build_ffn(embeddings, + model.mm_0_w, model.mm_0_b, + nullptr, nullptr, + model.mm_1_w, model.mm_1_b, + FFN_GELU, + -1); + + if (use_window_attn) { + window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / 4); + ggml_set_name(window_idx, "window_idx"); + ggml_set_input(window_idx); + + // embeddings shape: [n_embd, n_patches_x * n_patches_y, batch_size] + GGML_ASSERT(batch_size == 1); + embeddings = ggml_reshape_2d(ctx0, embeddings, hparams.projection_dim, n_patches_x * n_patches_y / 4); + embeddings = ggml_get_rows(ctx0, embeddings, window_idx); + embeddings = ggml_reshape_3d(ctx0, embeddings, hparams.projection_dim, n_patches_x * n_patches_y / 4, batch_size); + } + + // build the graph + ggml_build_forward_expand(gf, embeddings); + + return gf; +} diff --git a/tools/mtmd/models/qwen3vl.cpp b/tools/mtmd/models/qwen3vl.cpp new file mode 100644 index 0000000000..35a42cb84d --- /dev/null +++ b/tools/mtmd/models/qwen3vl.cpp @@ -0,0 +1,191 @@ +#include "models.h" + +ggml_cgraph * clip_graph_qwen3vl::build() { + GGML_ASSERT(model.patch_bias != nullptr); + GGML_ASSERT(model.position_embeddings != nullptr); + GGML_ASSERT(model.class_embedding == nullptr); + + const int batch_size = 1; + const int n_pos = n_patches; + const int num_position_ids = n_pos * 4; // m-rope requires 4 dim per position + + norm_type norm_t = NORM_TYPE_NORMAL; + + int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4}; + + ggml_tensor * inp_raw = build_inp_raw(); + ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); + + GGML_ASSERT(img.nx % (patch_size * 2) == 0); + GGML_ASSERT(img.ny % (patch_size * 2) == 0); + + // second conv dimension + { + auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1); + inp = ggml_add(ctx0, inp, inp_1); + + inp = ggml_permute(ctx0, inp, 1, 2, 0, 3); // [w, h, c, b] -> [c, w, h, b] + inp = ggml_cont_4d( + ctx0, inp, + n_embd * 2, n_patches_x / 2, n_patches_y, batch_size); + inp = ggml_reshape_4d( + ctx0, inp, + n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2)); + inp = ggml_permute(ctx0, inp, 0, 2, 1, 3); + inp = ggml_cont_3d( + ctx0, inp, + n_embd, n_patches_x * n_patches_y, batch_size); + } + + // add patch bias + if (model.patch_bias != nullptr) { + inp = ggml_add(ctx0, inp, model.patch_bias); + cb(inp, "patch_bias", -1); + } + + // calculate absolute position embedding and apply + ggml_tensor * learned_pos_embd = resize_position_embeddings(); + learned_pos_embd = ggml_cont_4d( + ctx0, learned_pos_embd, + n_embd * 2, n_patches_x / 2, n_patches_y, batch_size); + learned_pos_embd = ggml_reshape_4d( + ctx0, learned_pos_embd, + n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2)); + learned_pos_embd = ggml_permute(ctx0, learned_pos_embd, 0, 2, 1, 3); + learned_pos_embd = ggml_cont_3d( + ctx0, learned_pos_embd, + n_embd, n_patches_x * n_patches_y, batch_size); + inp = ggml_add(ctx0, inp, learned_pos_embd); + cb(inp, "inp_pos_emb", -1); + + ggml_tensor * inpL = inp; + + ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids); + ggml_set_name(positions, "positions"); + ggml_set_input(positions); + + // pre-layernorm + if (model.pre_ln_w) { + inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1); + } + + // deepstack features (stack along the feature dimension), [n_embd * len(deepstack_layers), n_patches_x * n_patches_y, batch_size] + ggml_tensor * deepstack_features = nullptr; + const int merge_factor = hparams.n_merge > 0 ? hparams.n_merge * hparams.n_merge : 4; // default 2x2=4 for qwen3vl + + // loop over layers + for (int il = 0; il < n_layer; il++) { + auto & layer = model.layers[il]; + + ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states + + // layernorm1 + cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il); + cb(cur, "ln1", il); + + // self-attention + { + cur = ggml_mul_mat(ctx0, layer.qkv_w, cur); + cur = ggml_add(ctx0, cur, layer.qkv_b); + + ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, + /* nb1 */ ggml_row_size(cur->type, d_head), + /* nb2 */ cur->nb[1], + /* offset */ 0); + + ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, + /* nb1 */ ggml_row_size(cur->type, d_head), + /* nb2 */ cur->nb[1], + /* offset */ ggml_row_size(cur->type, n_embd)); + + ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, + /* nb1 */ ggml_row_size(cur->type, d_head), + /* nb2 */ cur->nb[1], + /* offset */ ggml_row_size(cur->type, 2 * n_embd)); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + // apply M-RoPE + Qcur = ggml_rope_multi( + ctx0, Qcur, positions, nullptr, + d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); + Kcur = ggml_rope_multi( + ctx0, Kcur, positions, nullptr, + d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); + + cb(Qcur, "Qcur_rope", il); + cb(Kcur, "Kcur_rope", il); + + cur = build_attn(layer.o_w, layer.o_b, + Qcur, Kcur, Vcur, nullptr, kq_scale, il); + cb(cur, "attn_out", il); + } + + // re-add the layer input, e.g., residual + cur = ggml_add(ctx0, cur, inpL); + + inpL = cur; // inpL = residual, cur = hidden_states + + cb(cur, "ffn_inp", il); + + // layernorm2 + cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il); + cb(cur, "ffn_inp_normed", il); + + // ffn + cur = build_ffn(cur, + layer.ff_up_w, layer.ff_up_b, + layer.ff_gate_w, layer.ff_gate_b, + layer.ff_down_w, layer.ff_down_b, + hparams.ffn_op, il); + + cb(cur, "ffn_out", il); + + // residual 2 + cur = ggml_add(ctx0, inpL, cur); + cb(cur, "layer_out", il); + + if (layer.has_deepstack()) { + ggml_tensor * feat = ggml_reshape_3d(ctx0, cur, n_embd * merge_factor, n_pos / merge_factor, batch_size); + feat = build_norm(feat, layer.deepstack_norm_w, layer.deepstack_norm_b, norm_t, eps, il); + feat = build_ffn(feat, + layer.deepstack_fc1_w, layer.deepstack_fc1_b, + nullptr, nullptr, + layer.deepstack_fc2_w, layer.deepstack_fc2_b, + ffn_op_type::FFN_GELU, il); + + if(!deepstack_features) { + deepstack_features = feat; + } else { + // concat along the feature dimension + deepstack_features = ggml_concat(ctx0, deepstack_features, feat, 0); + } + } + + inpL = cur; + } + + // post-layernorm + if (model.post_ln_w) { + inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, n_layer); + } + + // multimodal projection + ggml_tensor * embeddings = inpL; + embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, n_pos / 4, batch_size); + + embeddings = build_ffn(embeddings, + model.mm_0_w, model.mm_0_b, + nullptr, nullptr, + model.mm_1_w, model.mm_1_b, + ffn_op_type::FFN_GELU, -1); + + embeddings = ggml_concat(ctx0, embeddings, deepstack_features, 0); // concat along the feature dimension + + // build the graph + ggml_build_forward_expand(gf, embeddings); + + return gf; +} diff --git a/tools/mtmd/models/siglip.cpp b/tools/mtmd/models/siglip.cpp new file mode 100644 index 0000000000..ef094cfd0e --- /dev/null +++ b/tools/mtmd/models/siglip.cpp @@ -0,0 +1,81 @@ +#include "models.h" + +ggml_cgraph * clip_graph_siglip::build() { + ggml_tensor * inp = build_inp(); + + ggml_tensor * learned_pos_embd = model.position_embeddings; + if (proj_type == PROJECTOR_TYPE_LFM2) { + learned_pos_embd = resize_position_embeddings(); + } + + ggml_tensor * cur = build_vit( + inp, n_patches, + NORM_TYPE_NORMAL, + hparams.ffn_op, + learned_pos_embd, + nullptr); + + if (proj_type == PROJECTOR_TYPE_GEMMA3) { + const int batch_size = 1; + GGML_ASSERT(n_patches_x == n_patches_y); + const int patches_per_image = n_patches_x; + const int kernel_size = hparams.n_merge; + + cur = ggml_transpose(ctx0, cur); + cur = ggml_cont_4d(ctx0, cur, patches_per_image, patches_per_image, n_embd, batch_size); + + // doing a pool2d to reduce the number of output tokens + cur = ggml_pool_2d(ctx0, cur, GGML_OP_POOL_AVG, kernel_size, kernel_size, kernel_size, kernel_size, 0, 0); + cur = ggml_reshape_3d(ctx0, cur, cur->ne[0] * cur->ne[0], n_embd, batch_size); + cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur)); + + // apply norm before projection + cur = ggml_rms_norm(ctx0, cur, eps); + cur = ggml_mul(ctx0, cur, model.mm_soft_emb_norm_w); + + // apply projection + cur = ggml_mul_mat(ctx0, + ggml_cont(ctx0, ggml_transpose(ctx0, model.mm_input_proj_w)), + cur); + + } else if (proj_type == PROJECTOR_TYPE_IDEFICS3) { + // pixel_shuffle + // https://github.com/huggingface/transformers/blob/0a950e0bbe1ed58d5401a6b547af19f15f0c195e/src/transformers/models/idefics3/modeling_idefics3.py#L578 + const int scale_factor = model.hparams.n_merge; + cur = build_patch_merge_permute(cur, scale_factor); + cur = ggml_mul_mat(ctx0, model.projection, cur); + + } else if (proj_type == PROJECTOR_TYPE_LFM2) { + // pixel unshuffle block + const int scale_factor = model.hparams.n_merge; + cur = build_patch_merge_permute(cur, scale_factor); + + // projection + cur = ggml_norm(ctx0, cur, 1e-5); // default nn.LayerNorm + cur = ggml_mul(ctx0, cur, model.mm_input_norm_w); + cur = ggml_add(ctx0, cur, model.mm_input_norm_b); + + cur = build_ffn(cur, + model.mm_1_w, model.mm_1_b, + nullptr, nullptr, + model.mm_2_w, model.mm_2_b, + FFN_GELU, + -1); + + } else if (proj_type == PROJECTOR_TYPE_JANUS_PRO) { + cur = build_ffn(cur, + model.mm_0_w, model.mm_0_b, + nullptr, nullptr, + model.mm_1_w, model.mm_1_b, + hparams.ffn_op, + -1); + + } else { + GGML_ABORT("SigLIP: Unsupported projector type"); + } + + // build the graph + ggml_build_forward_expand(gf, cur); + + return gf; +} diff --git a/tools/mtmd/models/whisper-enc.cpp b/tools/mtmd/models/whisper-enc.cpp new file mode 100644 index 0000000000..2870d854ab --- /dev/null +++ b/tools/mtmd/models/whisper-enc.cpp @@ -0,0 +1,106 @@ +#include "models.h" + +ggml_cgraph * clip_graph_whisper_enc::build() { + const int n_frames = img.nx; + const int n_pos = n_frames / 2; + GGML_ASSERT(model.position_embeddings->ne[1] >= n_pos); + + ggml_tensor * inp = build_inp_raw(1); + + // conv1d block + { + // convolution + gelu + ggml_tensor * cur = ggml_conv_1d_ph(ctx0, model.conv1d_1_w, inp, 1, 1); + cur = ggml_add(ctx0, cur, model.conv1d_1_b); + + cur = ggml_gelu_erf(ctx0, cur); + + cur = ggml_conv_1d_ph(ctx0, model.conv1d_2_w, cur, 2, 1); + cur = ggml_add(ctx0, cur, model.conv1d_2_b); + + cur = ggml_gelu_erf(ctx0, cur); + // transpose + inp = ggml_cont(ctx0, ggml_transpose(ctx0, cur)); + cb(inp, "after_conv1d", -1); + } + + // sanity check (only check one layer, but it should be the same for all) + GGML_ASSERT(model.layers[0].ln_1_w && model.layers[0].ln_1_b); + GGML_ASSERT(model.layers[0].ln_2_w && model.layers[0].ln_2_b); + GGML_ASSERT(model.layers[0].q_b); + GGML_ASSERT(model.layers[0].v_b); + GGML_ASSERT(!model.layers[0].k_b); // no bias for k + + ggml_tensor * pos_embd_selected = ggml_view_2d( + ctx0, model.position_embeddings, + model.position_embeddings->ne[0], n_pos, + model.position_embeddings->nb[1], 0 + ); + ggml_tensor * cur = build_vit( + inp, n_pos, + NORM_TYPE_NORMAL, + hparams.ffn_op, + pos_embd_selected, + nullptr); + + cb(cur, "after_transformer", -1); + + if (model.audio_has_stack_frames()) { + // StackAudioFrames + // https://huggingface.co/fixie-ai/ultravox-v0_5-llama-3_2-1b/blob/main/ultravox_model.py + cur = build_stack(cur, hparams.proj_stack_factor, n_embd); + cb(cur, "after_stacked", -1); + } + + if (proj_type == PROJECTOR_TYPE_ULTRAVOX) { + // UltravoxProjector + // pre-norm + cur = ggml_rms_norm(ctx0, cur, 1e-6); + cur = ggml_mul(ctx0, cur, model.mm_norm_pre_w); + + // ffn in + cur = ggml_mul_mat(ctx0, model.mm_1_w, cur); + + // swiglu + // see SwiGLU in ultravox_model.py, the second half passed through is silu, not the first half + cur = ggml_swiglu_swapped(ctx0, cur); + + // mid-norm + cur = ggml_rms_norm(ctx0, cur, 1e-6); + cur = ggml_mul(ctx0, cur, model.mm_norm_mid_w); + + // ffn out + cur = ggml_mul_mat(ctx0, model.mm_2_w, cur); + + } else if (proj_type == PROJECTOR_TYPE_QWEN2A) { + // projector + cur = ggml_mul_mat(ctx0, model.mm_fc_w, cur); + cur = ggml_add(ctx0, cur, model.mm_fc_b); + + } else if (proj_type == PROJECTOR_TYPE_VOXTRAL) { + // projector + cur = build_ffn(cur, + model.mm_1_w, model.mm_1_b, + nullptr, nullptr, + model.mm_2_w, model.mm_2_b, + FFN_GELU_ERF, + -1); + + } else if (proj_type == PROJECTOR_TYPE_GLMA) { + cur = ggml_norm(ctx0, cur, hparams.eps); + cur = ggml_mul(ctx0, cur, model.mm_norm_pre_w); + cur = ggml_add(ctx0, cur, model.mm_norm_pre_b); + cur = build_stack(cur, hparams.proj_stack_factor, n_embd); + cur = build_ffn(cur, model.mm_1_w, model.mm_1_b, nullptr, nullptr, model.mm_2_w, model.mm_2_b, hparams.ffn_op, 0); + cur = ggml_concat(ctx0, model.mm_boi, cur, 1); + cur = ggml_concat(ctx0, cur, model.mm_eoi, 1); + } else { + GGML_ABORT("%s: unknown projector type", __func__); + } + + cb(cur, "projected", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; +} diff --git a/tools/mtmd/mtmd-audio.cpp b/tools/mtmd/mtmd-audio.cpp index 4d053895cd..f68829a61a 100644 --- a/tools/mtmd/mtmd-audio.cpp +++ b/tools/mtmd/mtmd-audio.cpp @@ -11,63 +11,149 @@ // most of the code here is copied from whisper.cpp -// align x to upper multiple of n -#define _ALIGN(x, n) ((((x) + (n) - 1) / (n)) * (n)) +constexpr bool DEBUG = false; -namespace whisper_preprocessor { +struct mtmd_audio_mel_filters { + int32_t n_mel; + int32_t n_fft; -#define SIN_COS_N_COUNT WHISPER_N_FFT -namespace { -struct whisper_global_cache { - // In FFT, we frequently use sine and cosine operations with the same values. - // We can use precalculated values to speed up the process. - float sin_vals[SIN_COS_N_COUNT]; - float cos_vals[SIN_COS_N_COUNT]; + std::vector data; +}; - // Hann window (Use cosf to eliminate difference) - // ref: https://pytorch.org/docs/stable/generated/torch.hann_window.html - // ref: https://github.com/openai/whisper/blob/main/whisper/audio.py#L147 - float hann_window[WHISPER_N_FFT]; +// note: this global cache is shared among all preprocessors +// if we want to use multiple preprocessors at the same time, +// we will need to enclose it in the preprocessor class in the future +static struct mtmd_audio_global_cache { + // precomputed sin/cos table for FFT + std::vector sin_vals; + std::vector cos_vals; - whisper_global_cache() { - fill_sin_cos_table(); - fill_hann_window(sizeof(hann_window)/sizeof(hann_window[0]), true, hann_window); - } + // hann window + std::vector hann_window; - void fill_sin_cos_table() { - for (int i = 0; i < SIN_COS_N_COUNT; i++) { - double theta = (2 * M_PI * i) / SIN_COS_N_COUNT; + // mel filter bank + mtmd_audio_mel_filters filters; + + void fill_sin_cos_table(int n) { + sin_vals.resize(n); + cos_vals.resize(n); + for (int i = 0; i < n; i++) { + double theta = (2 * M_PI * i) / n; sin_vals[i] = sinf(theta); cos_vals[i] = cosf(theta); } } - void fill_hann_window(int length, bool periodic, float * output) { + void fill_hann_window(int length, bool periodic) { + hann_window.resize(length); int offset = -1; if (periodic) { offset = 0; } for (int i = 0; i < length; i++) { - output[i] = 0.5 * (1.0 - cosf((2.0 * M_PI * i) / (length + offset))); + hann_window[i] = 0.5 * (1.0 - cosf((2.0 * M_PI * i) / (length + offset))); } } -} global_cache; -} + + // Build mel filterbank matrix [n_mel × n_fft_bins] at runtime. + // n_fft_bins must be (N_fft / 2 + 1). Example: if N_fft=512 -> n_fft_bins=257. + void fill_mel_filterbank_matrix( + int n_mel, + int n_fft, + int sample_rate, // e.g. 16000 + float fmin = 0.0f, // e.g. 0.0 + float fmax = -1.0f, // e.g. sr/2; pass -1 for auto + bool slaney_area_norm = true, + float scale = 1.0f // optional extra scaling; use 1.0f/1000.0f to mimic your code + ) { + GGML_ASSERT(n_mel > 0 && n_fft > 1); + if (fmax <= 0.0f) { + fmax = 0.5f * sample_rate; + } + + // Slaney scale (matches librosa default) + const double min_log_hz = 1000.0; + const double lin_slope = 3 / 200.; + const double min_log_mel = min_log_hz * lin_slope; + const double log_step = log(6.4) / 27.0; + auto hz_to_mel = [min_log_hz, lin_slope, log_step, min_log_mel](const double f_hz) -> double { + return (f_hz < min_log_hz) ? f_hz * lin_slope : min_log_mel + log(f_hz / min_log_hz) / log_step; + }; + auto mel_to_hz = [min_log_hz, lin_slope, log_step, min_log_mel](const double m) -> double { + return (m < min_log_mel) ? m / lin_slope : min_log_hz * exp((m - min_log_mel) * log_step); + }; + + // infer N_fft from n_fft_bins + const double bin_hz_step = double(sample_rate) / double(n_fft); + + // mel grid: n_mel + 2 edges + const double m_lo = hz_to_mel(fmin); + const double m_hi = hz_to_mel(fmax); + std::vector mel_pts(n_mel + 2); + for (int i = 0; i < n_mel + 2; ++i) { + mel_pts[i] = m_lo + (m_hi - m_lo) * (double(i) / (n_mel + 1)); + } + + // convert to Hz + std::vector hz_pts(n_mel + 2); + for (int i = 0; i < n_mel + 2; ++i) { + hz_pts[i] = mel_to_hz(mel_pts[i]); + } + + const int n_fft_bins = n_fft / 2 + 1; + + // filterbank + std::vector out(n_mel * n_fft_bins, 0); + for (int m = 0; m < n_mel; ++m) { + const double f_left = hz_pts[m]; + const double f_center = hz_pts[m + 1]; + const double f_right = hz_pts[m + 2]; + + const double denom_l = std::max(1e-30, f_center - f_left); + const double denom_r = std::max(1e-30, f_right - f_center); + const double enorm = slaney_area_norm ? (2.0 / std::max(1e-30, f_right - f_left)) : 1.0; + + for (int k = 0; k < n_fft_bins; ++k) { + const double f = k * bin_hz_step; + double w = 0.0; + if (f >= f_left && f <= f_center) { + w = (f - f_left) / denom_l; + } else if (f > f_center && f <= f_right) { + w = (f_right - f) / denom_r; + } + out[size_t(m) * size_t(n_fft_bins) + size_t(k)] = float(w * enorm * scale); + } + } + + filters.n_mel = n_mel; + filters.n_fft = n_fft; + filters.data = std::move(out); + + if (DEBUG) { // debug + for (size_t i = 0; i < filters.data.size(); ++i) { + if (filters.data[i] != 0.0f) { + printf("filters[%zu] = %f\n", i, filters.data[i] * 1000.0f); + } + } + } + } +} g_cache; // naive Discrete Fourier Transform // input is real-valued // output is complex-valued -static void dft(const float* in, int N, float* out) { - const int sin_cos_step = SIN_COS_N_COUNT / N; +static void dft(const float * in, int N, float * out) { + const int n_sin_cos_vals = g_cache.sin_vals.size(); + const int sin_cos_step = n_sin_cos_vals / N; for (int k = 0; k < N; k++) { float re = 0; float im = 0; for (int n = 0; n < N; n++) { - int idx = (k * n * sin_cos_step) % (SIN_COS_N_COUNT); // t = 2*M_PI*k*n/N - re += in[n]*global_cache.cos_vals[idx]; // cos(t) - im -= in[n]*global_cache.sin_vals[idx]; // sin(t) + int idx = (k * n * sin_cos_step) % (n_sin_cos_vals); // t = 2*M_PI*k*n/N + re += in[n] * g_cache.cos_vals[idx]; // cos(t) + im -= in[n] * g_cache.sin_vals[idx]; // sin(t) } out[k*2 + 0] = re; @@ -79,7 +165,8 @@ static void dft(const float* in, int N, float* out) { // poor man's implementation - use something better // input is real-valued // output is complex-valued -static void fft(float* in, int N, float* out) { +static void fft(float * in, int N, float * out) { + const int n_sin_cos_vals = g_cache.sin_vals.size(); if (N == 1) { out[0] = in[0]; out[1] = 0; @@ -106,11 +193,11 @@ static void fft(float* in, int N, float* out) { float* odd_fft = even_fft + N; fft(odd, half_N, odd_fft); - const int sin_cos_step = SIN_COS_N_COUNT / N; + const int sin_cos_step = n_sin_cos_vals / N; for (int k = 0; k < half_N; k++) { int idx = k * sin_cos_step; // t = 2*M_PI*k/N - float re = global_cache.cos_vals[idx]; // cos(t) - float im = -global_cache.sin_vals[idx]; // sin(t) + float re = g_cache.cos_vals[idx]; // cos(t) + float im = -g_cache.sin_vals[idx]; // sin(t) float re_odd = odd_fft[2*k + 0]; float im_odd = odd_fft[2*k + 1]; @@ -123,20 +210,34 @@ static void fft(float* in, int N, float* out) { } } +struct filter_params { + int32_t n_mel; + int32_t n_fft_bins; + int32_t hann_window_size; + int32_t hop_length; + int32_t sample_rate; + bool center_padding = false; + float preemph = 0.f; + bool use_natural_log = false; + bool norm_per_feature = false; +}; + static void log_mel_spectrogram_worker_thread(int ith, const float * hann, const std::vector & samples, int n_samples, int frame_size, int frame_step, int n_threads, - const whisper_filters & filters, whisper_mel & mel) { + const filter_params & params, mtmd_audio_mel & out) { std::vector fft_in(frame_size * 2, 0.0); std::vector fft_out(frame_size * 2 * 2 * 2); - int n_fft = filters.n_fft; + int n_fft_bins = params.n_fft_bins; int i = ith; - // make sure n_fft == 1 + (WHISPER_N_FFT / 2), bin_0 to bin_nyquist - WHISPER_ASSERT(n_fft == 1 + (frame_size / 2)); + const auto & filters = g_cache.filters; + // make sure n_fft == 1 + (WHISPER_N_FFT / 2), bin_0 to bin_nyquist + GGML_ASSERT(n_fft_bins == 1 + (frame_size / 2)); + GGML_ASSERT(g_cache.sin_vals.size() == g_cache.cos_vals.size()); // calculate FFT only when fft_in are not all zero - for (; i < std::min(n_samples / frame_step + 1, mel.n_len); i += n_threads) { + for (; i < std::min(n_samples / frame_step + 1, out.n_len); i += n_threads) { const int offset = i * frame_step; // apply Hann window (~10% faster) @@ -154,36 +255,39 @@ static void log_mel_spectrogram_worker_thread(int ith, const float * hann, const // Calculate modulus^2 of complex numbers // Use pow(fft_out[2 * j + 0], 2) + pow(fft_out[2 * j + 1], 2) causes inference quality problem? Interesting. - for (int j = 0; j < n_fft; j++) { + for (int j = 0; j < n_fft_bins; j++) { fft_out[j] = (fft_out[2 * j + 0] * fft_out[2 * j + 0] + fft_out[2 * j + 1] * fft_out[2 * j + 1]); } // mel spectrogram - for (int j = 0; j < mel.n_mel; j++) { + for (int j = 0; j < out.n_mel; j++) { double sum = 0.0; // unroll loop (suggested by GH user @lunixbochs) int k = 0; - for (k = 0; k < n_fft - 3; k += 4) { + for (k = 0; k < n_fft_bins - 3; k += 4) { + size_t idx = size_t(j) * size_t(n_fft_bins) + size_t(k); sum += - fft_out[k + 0] * filters.data[j * n_fft + k + 0] + - fft_out[k + 1] * filters.data[j * n_fft + k + 1] + - fft_out[k + 2] * filters.data[j * n_fft + k + 2] + - fft_out[k + 3] * filters.data[j * n_fft + k + 3]; + fft_out[k + 0] * filters.data[idx + 0] + + fft_out[k + 1] * filters.data[idx + 1] + + fft_out[k + 2] * filters.data[idx + 2] + + fft_out[k + 3] * filters.data[idx + 3]; } // handle n_fft remainder - for (; k < n_fft; k++) { - sum += fft_out[k] * filters.data[j * n_fft + k]; + for (; k < n_fft_bins; k++) { + sum += fft_out[k] * filters.data[j * n_fft_bins + k]; } - sum = log10(std::max(sum, 1e-10)); - mel.data[j * mel.n_len + i] = sum; + sum = params.use_natural_log + ? log(sum + 5.960464477539063e-08) + : log10(std::max(sum, 1e-10)); + out.data[j * out.n_len + i] = sum; } } // Otherwise fft_out are all zero - double sum = log10(1e-10); - for (; i < mel.n_len; i += n_threads) { - for (int j = 0; j < mel.n_mel; j++) { - mel.data[j * mel.n_len + i] = sum; + double sum = params.use_natural_log ? log(1e-10) : log10(1e-10); + for (; i < out.n_len; i += n_threads) { + for (int j = 0; j < out.n_mel; j++) { + out.data[j * out.n_len + i] = sum; } } } @@ -191,115 +295,212 @@ static void log_mel_spectrogram_worker_thread(int ith, const float * hann, const // ref: https://github.com/openai/whisper/blob/main/whisper/audio.py#L110-L157 static bool log_mel_spectrogram( const float * samples, - const int n_samples, - const int /*sample_rate*/, - const int frame_size, - const int frame_step, - const int n_mel, - const int n_threads, - const whisper_filters & filters, - const bool debug, - whisper_mel & mel) { + const int n_samples_in, + const int n_threads, + const filter_params & params, + mtmd_audio_mel & out) { //const int64_t t_start_us = ggml_time_us(); + out.n_len_org = n_samples_in; + int n_samples = n_samples_in; + // Hann window - WHISPER_ASSERT(frame_size == WHISPER_N_FFT && "Unsupported frame_size"); - const float * hann = global_cache.hann_window; + const float * hann = g_cache.hann_window.data(); + const int frame_size = (params.n_fft_bins - 1) * 2; + const int frame_step = params.hop_length; - // Calculate the length of padding - int64_t stage_1_pad = WHISPER_SAMPLE_RATE * 30; - int64_t stage_2_pad = frame_size / 2; - - // Initialize a vector and copy data from C array to it. + // Padding std::vector samples_padded; - samples_padded.resize(n_samples + stage_1_pad + stage_2_pad * 2); - std::copy(samples, samples + n_samples, samples_padded.begin() + stage_2_pad); + if (params.center_padding) { + const auto pad_amount = frame_size / 2; + samples_padded = std::vector(n_samples + 2 * pad_amount, 0); + std::copy(samples, samples + n_samples, samples_padded.data() + pad_amount); + samples = samples_padded.data(); + n_samples = samples_padded.size(); + } else { + // existing padding logic + int64_t stage_1_pad = params.sample_rate * 30; + int64_t stage_2_pad = frame_size / 2; + samples_padded.resize(n_samples + stage_1_pad + stage_2_pad * 2); + std::copy(samples, samples + n_samples, samples_padded.begin() + stage_2_pad); + // pad 30 seconds of zeros at the end of audio (480,000 samples) + reflective pad 200 samples at the end of audio + std::fill(samples_padded.begin() + n_samples + stage_2_pad, samples_padded.begin() + n_samples + stage_1_pad + 2 * stage_2_pad, 0); + // reflective pad 200 samples at the beginning of audio + if (n_samples < stage_2_pad + 1) { + // TODO: Handle short audio differently or return error + return false; + } + std::reverse_copy(samples + 1, samples + 1 + stage_2_pad, samples_padded.begin()); + } - // pad 30 seconds of zeros at the end of audio (480,000 samples) + reflective pad 200 samples at the end of audio - std::fill(samples_padded.begin() + n_samples + stage_2_pad, samples_padded.begin() + n_samples + stage_1_pad + 2 * stage_2_pad, 0); + // preemphasis + if (params.preemph) { + const int pad_amount = frame_size / 2; + const float preemph = 0.97f; + float prev = samples_padded[pad_amount]; + for (int i = pad_amount + 1; i + pad_amount < n_samples; ++i) { + float cur = samples_padded[i]; + samples_padded[i] = cur - preemph * prev; + prev = cur; + } + } - // reflective pad 200 samples at the beginning of audio - std::reverse_copy(samples + 1, samples + 1 + stage_2_pad, samples_padded.begin()); + // pad hann window if it's smaller than frame_size + // TODO: probably unnecessary here? (or better doing it in g_cache?) + std::vector hann_window_padded; + if (params.hann_window_size < frame_size) { + hann_window_padded.resize(frame_size); + const int padding = (frame_size - params.hann_window_size) / 2; + std::copy(hann, hann + params.hann_window_size, &hann_window_padded[padding]); + hann = hann_window_padded.data(); + } - mel.n_mel = n_mel; - // https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/SpectralOps.cpp#L936 - // Calculate number of frames + remove the last frame - mel.n_len = (samples_padded.size() - frame_size) / frame_step; - // Calculate semi-padded sample length to ensure compatibility - mel.n_len_org = 1 + (n_samples + stage_2_pad - frame_size) / frame_step; - mel.data.resize(mel.n_mel * mel.n_len); + + out.n_mel = params.n_mel; + out.n_len = (n_samples - frame_size) / frame_step + 1; + // TODO: handle these checks better + if (out.n_mel > 0 && (unsigned long)out.n_len > SIZE_MAX / out.n_mel) { + LOG_ERR("%s: size overflow\n", __func__); + return false; + } + if (n_samples < frame_size) { + LOG_ERR("%s: not enough samples after padding\n", __func__); + return false; + } + out.data.resize(out.n_mel * out.n_len); { std::vector workers(n_threads - 1); for (int iw = 0; iw < n_threads - 1; ++iw) { workers[iw] = std::thread( log_mel_spectrogram_worker_thread, iw + 1, hann, std::cref(samples_padded), - n_samples + stage_2_pad, frame_size, frame_step, n_threads, - std::cref(filters), std::ref(mel)); + n_samples, frame_size, frame_step, n_threads, + std::cref(params), std::ref(out)); } // main thread - log_mel_spectrogram_worker_thread(0, hann, samples_padded, n_samples + stage_2_pad, frame_size, frame_step, n_threads, filters, mel); - + log_mel_spectrogram_worker_thread(0, hann, samples_padded, n_samples, frame_size, frame_step, n_threads, params, out); for (int iw = 0; iw < n_threads - 1; ++iw) { workers[iw].join(); } } - // clamping and normalization - double mmax = -1e20; - for (int i = 0; i < mel.n_mel*mel.n_len; i++) { - if (mel.data[i] > mmax) { - mmax = mel.data[i]; + const int effective_n_len = n_samples_in / frame_step; + if (params.norm_per_feature) { + for (int i = 0; i < out.n_mel; i++) { + double mean = 0; + for (int j = 0; j < effective_n_len; ++j) { + mean += out.data[i * out.n_len + j]; + } + mean /= effective_n_len; + + double var = 0.0; + for (int j = 0; j < effective_n_len; ++j) { + const double value = out.data[i * out.n_len + j] - mean; + var += value * value; + } + var /= effective_n_len - 1; // unbiased + const double mstd = std::sqrt(var + 1e-5); + + for (int j = 0; j < effective_n_len; ++j) { + auto &value = out.data[i * out.n_len + j]; + value = (value - mean) / mstd; + } + + // pad the rest with zeros + for (int j = effective_n_len; j < out.n_len; ++j) { + out.data[i * out.n_len + j] = 0.0; + } } - } - - mmax -= 8.0; - - for (int i = 0; i < mel.n_mel*mel.n_len; i++) { - if (mel.data[i] < mmax) { - mel.data[i] = mmax; + } else { + // clamping and normalization + double mmax = -1e20; + for (int i = 0; i < out.n_mel*out.n_len; i++) { + if (out.data[i] > mmax) { + mmax = out.data[i]; + } } - mel.data[i] = (mel.data[i] + 4.0)/4.0; + mmax -= 8.0; + + for (int i = 0; i < out.n_mel*out.n_len; i++) { + if (out.data[i] < mmax) { + out.data[i] = mmax; + } + out.data[i] = (out.data[i] + 4.0)/4.0; + } } // Dump log_mel_spectrogram - if (debug) { + if (DEBUG) { std::ofstream outFile("log_mel_spectrogram.json"); outFile << "["; - for (uint64_t i = 0; i < mel.data.size() - 1; i++) { - outFile << mel.data[i] << ", "; + for (uint64_t i = 0; i < out.data.size() - 1; i++) { + outFile << out.data[i] << ", "; } - outFile << mel.data[mel.data.size() - 1] << "]"; + outFile << out.data[out.data.size() - 1] << "]"; outFile.close(); } return true; } -bool preprocess_audio( +// +// mtmd_audio_preprocessor_whisper +// + +void mtmd_audio_preprocessor_whisper::initialize() { + g_cache.fill_sin_cos_table(hparams.audio_n_fft); + g_cache.fill_hann_window(hparams.audio_window_len, true); + g_cache.fill_mel_filterbank_matrix( + hparams.n_mel_bins, + hparams.audio_n_fft, + hparams.audio_sample_rate); +} + +bool mtmd_audio_preprocessor_whisper::preprocess( const float * samples, size_t n_samples, - const whisper_filters & filters, - std::vector & output) { - + std::vector & output) { if (n_samples == 0) { // empty audio return false; } - whisper_mel out_full; + std::vector smpl; + // if input is too short, pad with zeros + // this is to avoid potential issues with stage1/2 padding in log_mel_spectrogram + // TODO: maybe handle this better + size_t min_samples = (size_t)hparams.audio_sample_rate * (hparams.audio_chunk_len + 1); // +1 second margin + if (n_samples < min_samples) { + smpl.resize(min_samples, 0.0f); + std::memcpy(smpl.data(), samples, n_samples * sizeof(float)); + samples = smpl.data(); + n_samples = smpl.size(); + } + + filter_params params; + params.n_mel = hparams.n_mel_bins; + params.n_fft_bins = 1 + (hparams.audio_n_fft / 2); + params.hann_window_size = hparams.audio_window_len; + params.hop_length = hparams.audio_hop_len; + params.sample_rate = hparams.audio_sample_rate; + params.center_padding = false; + params.preemph = 0.0f; // disabled + params.use_natural_log = false; + params.norm_per_feature = false; + + // make sure the global cache is initialized + GGML_ASSERT(!g_cache.sin_vals.empty()); + GGML_ASSERT(!g_cache.cos_vals.empty()); + GGML_ASSERT(!g_cache.filters.data.empty()); + + mtmd_audio_mel out_full; bool ok = log_mel_spectrogram( samples, n_samples, - COMMON_SAMPLE_RATE, - WHISPER_N_FFT, - WHISPER_HOP_LENGTH, - filters.n_mel, 4, // n_threads - filters, - false, // debug + params, out_full); if (!ok) { return false; @@ -307,7 +508,9 @@ bool preprocess_audio( // because the cgraph in clip.cpp only accepts 3000 frames each, we need to split the mel // we always expect the mel to have 3000 silent frames at the end - // printf("n_len %d\n", out_full.n_len); + if (DEBUG) { + printf("output: n_mel = %d, n_len = %d\n", out_full.n_mel, out_full.n_len); + } const size_t frames_per_chunk = 3000; GGML_ASSERT((size_t)out_full.n_len > frames_per_chunk); for (size_t off = 0; off < (size_t)out_full.n_len; off += frames_per_chunk) { @@ -316,7 +519,7 @@ bool preprocess_audio( break; // last uncomplete chunk will always be a padded chunk, safe to ignore } - whisper_mel out_chunk; + mtmd_audio_mel out_chunk; out_chunk.n_len = n_len; out_chunk.n_mel = out_full.n_mel; out_chunk.n_len_org = out_full.n_mel; // unused @@ -332,438 +535,3 @@ bool preprocess_audio( return true; } - -} // namespace whisper_preprocessor - - -// precalculated mel filter banks -// values are multiplied by 1000.0 to save space, and will be divided by 1000.0 in the end of the function -// -// generated from python code: -// -// from numpy import load -// data = load('mel_filters.npz') -// lst = data.files -// for item in lst: -// print(item) -// print(data[item].shape) -// n_mel = data[item].shape[0] -// n_fft = data[item].shape[1] -// for i, row in enumerate(data[item]): -// for j, val in enumerate(row): -// val = val * 1000.0 -// if val != 0: -// print(f"data[{i*n_fft + j}] = {val:.6f};") - -namespace whisper_precalc_filters { - -whisper_preprocessor::whisper_filters get_128_bins() { - whisper_preprocessor::whisper_filters filters; - filters.n_mel = 128; - filters.n_fft = 201; - std::vector data(filters.n_mel * filters.n_fft, 0.0f); - - data[1] = 12.37398665; - data[202] = 30.39256483; - data[404] = 24.74797331; - data[605] = 18.01857911; - data[807] = 37.12195903; - data[1008] = 5.64459199; - data[1009] = 6.72939420; - data[1210] = 36.03715822; - data[1412] = 19.10337992; - data[1613] = 23.66316877; - data[1815] = 31.47736564; - data[2016] = 11.28918398; - data[2017] = 1.08480197; - data[2218] = 41.68175161; - data[2420] = 13.45878839; - data[2621] = 29.30776216; - data[2823] = 25.83277412; - data[3024] = 16.93377644; - data[3226] = 38.20675984; - data[3427] = 4.55979025; - data[3428] = 7.81419594; - data[3629] = 34.95235741; - data[3831] = 20.18818259; - data[4032] = 22.57836796; - data[4234] = 32.56217018; - data[4435] = 10.20438317; - data[4436] = 2.16960395; - data[4637] = 40.59694707; - data[4839] = 14.54358920; - data[5040] = 28.22295949; - data[5242] = 26.91757679; - data[5443] = 15.84897563; - data[5645] = 39.29156065; - data[5846] = 3.47498828; - data[5847] = 8.89899861; - data[6048] = 33.86755288; - data[6250] = 21.27298526; - data[6451] = 21.49356715; - data[6653] = 33.64697099; - data[6854] = 9.11958050; - data[6855] = 3.25440569; - data[7056] = 39.51214626; - data[7258] = 15.62839188; - data[7459] = 27.13815868; - data[7661] = 28.00237760; - data[7862] = 14.76417296; - data[8064] = 40.37636518; - data[8265] = 2.38068704; - data[8266] = 10.20263787; - data[8467] = 31.61146119; - data[8669] = 24.54700135; - data[8870] = 15.32919332; - data[8871] = 1.66583748; - data[9072] = 36.72905266; - data[9274] = 20.09709924; - data[9475] = 16.93102531; - data[9476] = 2.90265540; - data[9677] = 32.84499049; - data[9879] = 23.52004871; - data[10080] = 11.03894413; - data[10081] = 10.72582975; - data[10282] = 22.71829173; - data[10484] = 32.27872774; - data[10685] = 0.11626833; - data[10686] = 22.85348251; - data[10887] = 8.56344029; - data[10888] = 14.97978810; - data[11089] = 15.51398356; - data[11090] = 8.51490628; - data[11291] = 21.10680379; - data[11292] = 3.32652032; - data[11493] = 25.47064796; - data[11695] = 27.35907957; - data[11896] = 0.65853616; - data[11897] = 23.83812517; - data[12098] = 3.44359246; - data[12099] = 21.22455277; - data[12300] = 5.35842171; - data[12301] = 19.42555793; - data[12502] = 6.49324711; - data[12503] = 18.35542172; - data[12704] = 6.93138083; - data[12705] = 17.93504693; - data[12906] = 6.74968259; - data[12907] = 18.09151843; - data[13108] = 6.01899112; - data[13109] = 18.75767298; - data[13310] = 4.80452832; - data[13311] = 19.87172849; - data[13512] = 3.16627859; - data[13513] = 21.37690969; - data[13514] = 1.25317345; - data[13714] = 1.15934468; - data[13715] = 20.80361731; - data[13716] = 4.04486805; - data[13917] = 17.55363122; - data[13918] = 7.08320038; - data[14119] = 14.07538634; - data[14120] = 10.32655034; - data[14321] = 10.40921453; - data[14322] = 13.73696327; - data[14523] = 6.59187697; - data[14524] = 17.27988198; - data[14525] = 1.46804214; - data[14725] = 2.65681883; - data[14726] = 18.09193194; - data[14727] = 5.85655728; - data[14928] = 13.34277913; - data[14929] = 10.28267574; - data[15130] = 8.56800377; - data[15131] = 14.72230814; - data[15132] = 1.04039861; - data[15332] = 3.79085587; - data[15333] = 17.14678481; - data[15334] = 6.11609267; - data[15535] = 11.75929047; - data[15536] = 11.13393717; - data[15737] = 6.43857848; - data[15738] = 16.07806236; - data[15739] = 4.23917221; - data[15939] = 1.19989377; - data[15940] = 12.75671553; - data[15941] = 9.65298992; - data[16142] = 7.06935255; - data[16143] = 14.94054683; - data[16144] = 4.19024844; - data[16344] = 1.51483389; - data[16345] = 12.00899947; - data[16346] = 9.84823331; - data[16547] = 6.10224018; - data[16548] = 15.33857174; - data[16549] = 5.57676842; - data[16749] = 0.36827257; - data[16750] = 9.89749376; - data[16751] = 11.35340426; - data[16752] = 2.05122307; - data[16952] = 3.89297144; - data[16953] = 12.97352277; - data[16954] = 8.06631614; - data[17155] = 6.74493238; - data[17156] = 13.85874674; - data[17157] = 5.41190524; - data[17357] = 0.74220158; - data[17358] = 8.98779090; - data[17359] = 11.37871388; - data[17360] = 3.32958088; - data[17560] = 2.82313535; - data[17561] = 10.68049297; - data[17562] = 9.43340641; - data[17563] = 1.76325557; - data[17763] = 4.39018616; - data[17764] = 11.87758986; - data[17765] = 7.97005836; - data[17766] = 0.66104700; - data[17966] = 5.49466675; - data[17967] = 12.62953598; - data[17968] = 6.93987962; - data[18169] = 6.18401915; - data[18170] = 12.93473132; - data[18171] = 6.29778765; - data[18371] = 0.02325210; - data[18372] = 6.50206627; - data[18373] = 12.32661773; - data[18374] = 6.00216538; - data[18574] = 0.31548753; - data[18575] = 6.48925547; - data[18576] = 12.04130240; - data[18577] = 6.01462880; - data[18777] = 0.29979556; - data[18778] = 6.18288014; - data[18779] = 12.04272825; - data[18780] = 6.29981188; - data[18781] = 0.55689598; - data[18980] = 0.01120471; - data[18981] = 5.61729167; - data[18982] = 11.22337859; - data[18983] = 6.82516303; - data[18984] = 1.35264499; - data[19184] = 4.82410006; - data[19185] = 10.16623247; - data[19186] = 7.56075513; - data[19187] = 2.34590308; - data[19387] = 3.83235747; - data[19388] = 8.92296247; - data[19389] = 8.47910438; - data[19390] = 3.50978645; - data[19590] = 2.66873185; - data[19591] = 7.51965167; - data[19592] = 9.55500547; - data[19593] = 4.81966138; - data[19594] = 0.08431751; - data[19793] = 1.35767367; - data[19794] = 5.98019501; - data[19795] = 10.60271543; - data[19796] = 6.25298498; - data[19797] = 1.74059917; - data[19997] = 4.32644226; - data[19998] = 8.73131864; - data[19999] = 7.78916525; - data[20000] = 3.48923868; - data[20200] = 2.57835095; - data[20201] = 6.77582854; - data[20202] = 9.40941647; - data[20203] = 5.31194592; - data[20204] = 1.21447595; - data[20403] = 0.75411191; - data[20404] = 4.75395704; - data[20405] = 8.75380263; - data[20406] = 7.19209015; - data[20407] = 3.28754401; - data[20607] = 2.68179690; - data[20608] = 6.49331464; - data[20609] = 9.11457930; - data[20610] = 5.39387390; - data[20611] = 1.67316827; - data[20810] = 0.57394296; - data[20811] = 4.20600036; - data[20812] = 7.83805829; - data[20813] = 7.52023002; - data[20814] = 3.97470826; - data[20815] = 0.42918732; - data[21014] = 1.90464477; - data[21015] = 5.36569161; - data[21016] = 8.82673822; - data[21017] = 6.27609482; - data[21018] = 2.89750961; - data[21218] = 2.89885257; - data[21219] = 6.19694078; - data[21220] = 8.56699049; - data[21221] = 5.34748193; - data[21222] = 2.12797290; - data[21421] = 0.44750227; - data[21422] = 3.59030394; - data[21423] = 6.73310598; - data[21424] = 7.77023612; - data[21425] = 4.70231380; - data[21426] = 1.63439126; - data[21625] = 1.01536023; - data[21626] = 4.01018746; - data[21627] = 7.00501446; - data[21628] = 7.23442994; - data[21629] = 4.31095669; - data[21630] = 1.38748321; - data[21829] = 1.33348850; - data[21830] = 4.18730825; - data[21831] = 7.04112789; - data[21832] = 6.93188375; - data[21833] = 4.14605811; - data[21834] = 1.36023236; - data[22033] = 1.42879714; - data[22034] = 4.14824858; - data[22035] = 6.86769979; - data[22036] = 6.83705276; - data[22037] = 4.18239459; - data[22038] = 1.52773573; - data[22237] = 1.32610439; - data[22238] = 3.91751388; - data[22239] = 6.50892360; - data[22240] = 6.92639686; - data[22241] = 4.39672917; - data[22242] = 1.86706171; - data[22441] = 1.04827771; - data[22442] = 3.51767405; - data[22443] = 5.98707050; - data[22444] = 7.17824046; - data[22445] = 4.76767914; - data[22446] = 2.35711760; - data[22645] = 0.61636406; - data[22646] = 2.96949223; - data[22647] = 5.32262027; - data[22648] = 7.57265091; - data[22649] = 5.27558755; - data[22650] = 2.97852419; - data[22651] = 0.68146095; - data[22849] = 0.04971400; - data[22850] = 2.29204819; - data[22851] = 4.53438237; - data[22852] = 6.77671656; - data[22853] = 5.90240723; - data[22854] = 3.71349836; - data[22855] = 1.52458926; - data[23054] = 1.50285335; - data[23055] = 3.63961048; - data[23056] = 5.77636715; - data[23057] = 6.63159089; - data[23058] = 4.54574358; - data[23059] = 2.45989650; - data[23060] = 0.37404924; - data[23258] = 0.61795861; - data[23259] = 2.65410915; - data[23260] = 4.69025923; - data[23261] = 6.72641024; - data[23262] = 5.46034705; - data[23263] = 3.47270933; - data[23264] = 1.48507138; - data[23463] = 1.59233576; - data[23464] = 3.53261665; - data[23465] = 5.47289755; - data[23466] = 6.44368259; - data[23467] = 4.54962999; - data[23468] = 2.65557761; - data[23469] = 0.76152512; - data[23667] = 0.46749352; - data[23668] = 2.31641904; - data[23669] = 4.16534441; - data[23670] = 6.01426978; - data[23671] = 5.67844696; - data[23672] = 3.87357362; - data[23673] = 2.06870004; - data[23674] = 0.26382666; - data[23872] = 1.05349103; - data[23873] = 2.81536230; - data[23874] = 4.57723346; - data[23875] = 6.33910485; - data[23876] = 5.12815686; - data[23877] = 3.40826320; - data[23878] = 1.68837002; - data[24077] = 1.43350090; - data[24078] = 3.11241671; - data[24079] = 4.79133241; - data[24080] = 6.40943693; - data[24081] = 4.77052201; - data[24082] = 3.13160778; - data[24083] = 1.49269309; - data[24281] = 0.02932359; - data[24282] = 1.62918994; - data[24283] = 3.22905602; - data[24284] = 4.82892245; - data[24285] = 6.14671456; - data[24286] = 4.58496623; - data[24287] = 3.02321767; - data[24288] = 1.46146910; - data[24486] = 0.13601698; - data[24487] = 1.66055572; - data[24488] = 3.18509457; - data[24489] = 4.70963307; - data[24490] = 6.04072399; - data[24491] = 4.55250870; - data[24492] = 3.06429295; - data[24493] = 1.57607743; - data[24494] = 0.08786193; - data[24691] = 0.09328097; - data[24692] = 1.54603878; - data[24693] = 2.99879676; - data[24694] = 4.45155473; - data[24695] = 5.90431225; - data[24696] = 4.65566106; - data[24697] = 3.23751615; - data[24698] = 1.81937125; - data[24699] = 0.40122634; - data[24897] = 1.30262633; - data[24898] = 2.68698297; - data[24899] = 4.07133950; - data[24900] = 5.45569602; - data[24901] = 4.87832492; - data[24902] = 3.52695142; - data[24903] = 2.17557792; - data[24904] = 0.82420459; - data[25102] = 0.94595028; - data[25103] = 2.26512621; - data[25104] = 3.58430226; - data[25105] = 4.90347855; - data[25106] = 5.20569785; - data[25107] = 3.91795207; - data[25108] = 2.63020652; - data[25109] = 1.34246063; - data[25110] = 0.05471494; - data[25307] = 0.49037894; - data[25308] = 1.74744334; - data[25309] = 3.00450763; - data[25310] = 4.26157191; - data[25311] = 5.51863620; - data[25312] = 4.39707236; - data[25313] = 3.16995848; - data[25314] = 1.94284460; - data[25315] = 0.71573065; - data[25513] = 1.14698056; - data[25514] = 2.34485767; - data[25515] = 3.54273478; - data[25516] = 4.74061165; - data[25517] = 4.95198462; - data[25518] = 3.78264743; - data[25519] = 2.61331047; - data[25520] = 1.44397374; - data[25521] = 0.27463681; - data[25718] = 0.47569509; - data[25719] = 1.61717169; - data[25720] = 2.75864848; - data[25721] = 3.90012516; - data[25722] = 5.04160160; - data[25723] = 4.45712078; - data[25724] = 3.34284059; - data[25725] = 2.22856039; - data[25726] = 1.11428020; - - for (auto & val : data) { - val /= 1000.0f; - } - - filters.data = std::move(data); - return filters; -} - -} // namespace whisper_precalc_filters diff --git a/tools/mtmd/mtmd-audio.h b/tools/mtmd/mtmd-audio.h index b7b940affb..1b454337cb 100644 --- a/tools/mtmd/mtmd-audio.h +++ b/tools/mtmd/mtmd-audio.h @@ -1,23 +1,15 @@ #pragma once #include "ggml.h" +#include "clip-model.h" #include #include #include -#define WHISPER_ASSERT GGML_ASSERT +#define MTMD_INTERNAL_HEADER -#define WHISPER_SAMPLE_RATE 16000 -#define WHISPER_N_FFT 400 -#define WHISPER_HOP_LENGTH 160 -#define WHISPER_CHUNK_SIZE 30 - -#define COMMON_SAMPLE_RATE 16000 - -namespace whisper_preprocessor { - -struct whisper_mel { +struct mtmd_audio_mel { int n_len; int n_len_org; int n_mel; @@ -25,23 +17,18 @@ struct whisper_mel { std::vector data; }; -struct whisper_filters { - int32_t n_mel; - int32_t n_fft; +struct mtmd_audio_preprocessor { + const clip_hparams & hparams; - std::vector data; + mtmd_audio_preprocessor(const clip_ctx * ctx): hparams(*clip_get_hparams(ctx)) {} + + virtual ~mtmd_audio_preprocessor() = default; + virtual void initialize() = 0; // NOT thread-safe + virtual bool preprocess(const float * samples, size_t n_samples, std::vector & output) = 0; }; -bool preprocess_audio( - const float * samples, - size_t n_samples, - const whisper_filters & filters, - std::vector & output); - -} // namespace whisper_preprocessor - -namespace whisper_precalc_filters { - -whisper_preprocessor::whisper_filters get_128_bins(); - -} // namespace whisper_precalc_filters +struct mtmd_audio_preprocessor_whisper : mtmd_audio_preprocessor { + mtmd_audio_preprocessor_whisper(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {} + void initialize() override; + bool preprocess(const float * samples, size_t n_samples, std::vector & output) override; +}; diff --git a/tools/mtmd/mtmd-cli.cpp b/tools/mtmd/mtmd-cli.cpp index b5bbc6536b..332d2049e5 100644 --- a/tools/mtmd/mtmd-cli.cpp +++ b/tools/mtmd/mtmd-cli.cpp @@ -65,7 +65,7 @@ static void sigint_handler(int signo) { struct mtmd_cli_context { mtmd::context_ptr ctx_vision; - common_init_result llama_init; + common_init_result_ptr llama_init; llama_model * model; llama_context * lctx; @@ -89,8 +89,8 @@ struct mtmd_cli_context { llama_pos n_past = 0; mtmd_cli_context(common_params & params) : llama_init(common_init_from_params(params)) { - model = llama_init.model.get(); - lctx = llama_init.context.get(); + model = llama_init->model(); + lctx = llama_init->context(); vocab = llama_model_get_vocab(model); smpl = common_sampler_init(model, params.sampling); n_threads = params.cpuparams.n_threads; @@ -270,6 +270,7 @@ int main(int argc, char ** argv) { ggml_time_init(); common_params params; + params.use_jinja = false; // disable jinja by default params.sampling.temp = 0.2; // lower temp by default for better quality if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_MTMD, show_additional_info)) { @@ -310,11 +311,16 @@ int main(int argc, char ** argv) { if (g_is_interrupted) return 130; + LOG_WRN("WARN: This is an experimental CLI for testing multimodal capability.\n"); + LOG_WRN(" For normal use cases, please use the standard llama-cli\n"); + if (is_single_turn) { g_is_generating = true; if (params.prompt.find(mtmd_default_marker()) == std::string::npos) { for (size_t i = 0; i < params.image.size(); i++) { - params.prompt += mtmd_default_marker(); + // most models require the marker before each image + // ref: https://github.com/ggml-org/llama.cpp/pull/17616 + params.prompt = mtmd_default_marker() + params.prompt; } } common_chat_msg msg; @@ -349,11 +355,11 @@ int main(int argc, char ** argv) { while (!g_is_interrupted) { g_is_generating = false; LOG("\n> "); - console::set_display(console::user_input); + console::set_display(DISPLAY_TYPE_USER_INPUT); std::string line; console::readline(line, false); if (g_is_interrupted) break; - console::set_display(console::reset); + console::set_display(DISPLAY_TYPE_RESET); line = string_strip(line); if (line.empty()) { continue; diff --git a/tools/mtmd/mtmd-helper.cpp b/tools/mtmd/mtmd-helper.cpp index f0891bba30..902a4b456d 100644 --- a/tools/mtmd/mtmd-helper.cpp +++ b/tools/mtmd/mtmd-helper.cpp @@ -32,6 +32,10 @@ #define STB_IMAGE_IMPLEMENTATION #include "stb/stb_image.h" +#ifdef MTMD_INTERNAL_HEADER +#error "mtmd-helper is a public library outside of mtmd. it must not include internal headers" +#endif + // // internal logging functions // diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp index d06fa42e61..c63f299cd9 100644 --- a/tools/mtmd/mtmd.cpp +++ b/tools/mtmd/mtmd.cpp @@ -151,8 +151,7 @@ struct mtmd_context { // string template for slice image delimiters with row/col (idefics3) std::string sli_img_start_tmpl; - // for whisper, we pre-calculate the mel filter bank - whisper_preprocessor::whisper_filters w_filters; + std::unique_ptr audio_preproc; // TODO @ngxson : add timings @@ -317,14 +316,25 @@ struct mtmd_context { GGML_ASSERT(ctx_a != nullptr); projector_type proj = clip_get_projector_type(ctx_a); - if (clip_has_whisper_encoder(ctx_a)) { - // TODO @ngxson : check if model n_mel is 128 or 80 - w_filters = whisper_precalc_filters::get_128_bins(); - } - LOG_WRN("%s: audio input is in experimental stage and may have reduced quality:\n" " https://github.com/ggml-org/llama.cpp/discussions/13759\n", __func__); + // set preprocessor + switch (proj) { + case PROJECTOR_TYPE_QWEN2A: + case PROJECTOR_TYPE_QWEN25O: + case PROJECTOR_TYPE_ULTRAVOX: + case PROJECTOR_TYPE_VOXTRAL: + audio_preproc = std::make_unique(ctx_a); + break; + default: + GGML_ABORT("unsupported audio projector type"); + } + + // initialize audio preprocessor + audio_preproc->initialize(); + + // set special tokens if (proj == PROJECTOR_TYPE_QWEN2A) { // <|audio_bos|> ... (embeddings) ... <|audio_eos|> aud_beg = "<|audio_bos|>"; @@ -653,11 +663,10 @@ struct mtmd_tokenizer { } // preprocess audio - GGML_ASSERT(ctx->w_filters.n_mel); // make sure we have filter preloaded - std::vector mel_spec_chunks; + std::vector mel_spec_chunks; const float * samples = (const float *)bitmap->data.data(); size_t n_samples = bitmap->data.size() / sizeof(float); - bool ok = whisper_preprocessor::preprocess_audio(samples, n_samples, ctx->w_filters, mel_spec_chunks); + bool ok = ctx->audio_preproc->preprocess(samples, n_samples, mel_spec_chunks); if (!ok) { LOG_ERR("Unable to preprocess audio\n"); return 2; @@ -863,8 +872,7 @@ int mtmd_get_audio_bitrate(mtmd_context * ctx) { if (!ctx->ctx_a) { return -1; } - // for now, we assume that all audio models have the same bitrate - return 16000; // 16kHz + return clip_get_hparams(ctx->ctx_a)->audio_sample_rate; } // diff --git a/tools/mtmd/mtmd.h b/tools/mtmd/mtmd.h index b3df24c299..9f7e861e92 100644 --- a/tools/mtmd/mtmd.h +++ b/tools/mtmd/mtmd.h @@ -22,6 +22,11 @@ * Issues related to API usage may receive lower priority support. * * For the usage, see an example in mtmd-cli.cpp + * + * For contributors: + * - Make sure the C API is aligned with the libllama C API (as in llama.h) + * - Do not include model name (e.g., qwen, gemma) in the API, use generic terms instead + * - Keep the API minimal, do not expose internal details unless necessary */ #ifdef LLAMA_SHARED diff --git a/tools/mtmd/tests.sh b/tools/mtmd/tests.sh index 472f7d821c..82b486ec93 100755 --- a/tools/mtmd/tests.sh +++ b/tools/mtmd/tests.sh @@ -32,23 +32,32 @@ fi arr_prefix=() arr_hf=() -arr_tmpl=() # chat template +arr_extra_args=() arr_file=() add_test_vision() { local hf=$1 - local tmpl=${2:-""} # default to empty string if not provided + shift + local extra_args="" + if [ $# -gt 0 ]; then + extra_args=$(printf " %q" "$@") + fi arr_prefix+=("[vision]") arr_hf+=("$hf") - arr_tmpl+=("$tmpl") + arr_extra_args+=("$extra_args") arr_file+=("test-1.jpeg") } add_test_audio() { local hf=$1 + shift + local extra_args="" + if [ $# -gt 0 ]; then + extra_args=$(printf " %q" "$@") + fi arr_prefix+=("[audio] ") arr_hf+=("$hf") - arr_tmpl+=("") # no need for chat tmpl + arr_extra_args+=("$extra_args") arr_file+=("test-2.mp3") } @@ -56,9 +65,9 @@ add_test_vision "ggml-org/SmolVLM-500M-Instruct-GGUF:Q8_0" add_test_vision "ggml-org/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M" add_test_vision "ggml-org/SmolVLM2-500M-Video-Instruct-GGUF:Q8_0" add_test_vision "ggml-org/gemma-3-4b-it-GGUF:Q4_K_M" -add_test_vision "THUDM/glm-edge-v-5b-gguf:Q4_K_M" -add_test_vision "second-state/Llava-v1.5-7B-GGUF:Q2_K" "vicuna" -add_test_vision "cjpais/llava-1.6-mistral-7b-gguf:Q3_K_M" "vicuna" +add_test_vision "THUDM/glm-edge-v-5b-gguf:Q4_K_M" -p "name of the newspaper?<__media__>" +add_test_vision "second-state/Llava-v1.5-7B-GGUF:Q2_K" --chat-template vicuna +add_test_vision "cjpais/llava-1.6-mistral-7b-gguf:Q3_K_M" --chat-template vicuna add_test_vision "ibm-research/granite-vision-3.2-2b-GGUF:Q4_K_M" add_test_vision "second-state/MiniCPM-Llama3-V-2_5-GGUF:Q2_K" # model from openbmb is corrupted add_test_vision "openbmb/MiniCPM-V-2_6-gguf:Q2_K" @@ -79,7 +88,7 @@ add_test_audio "ggml-org/Voxtral-Mini-3B-2507-GGUF:Q4_K_M" # to test the big models, run: ./tests.sh big if [ "$RUN_BIG_TESTS" = true ]; then add_test_vision "ggml-org/pixtral-12b-GGUF:Q4_K_M" - add_test_vision "ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF" "mistral-v7" + add_test_vision "ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF" --chat-template mistral-v7 add_test_vision "ggml-org/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M" add_test_vision "ggml-org/Qwen2-VL-7B-Instruct-GGUF:Q4_K_M" add_test_vision "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M" @@ -89,7 +98,7 @@ if [ "$RUN_BIG_TESTS" = true ]; then add_test_vision "ggml-org/InternVL3-14B-Instruct-GGUF:Q4_K_M" add_test_vision "ggml-org/Qwen2.5-Omni-7B-GGUF:Q4_K_M" # add_test_vision "ggml-org/Qwen2.5-VL-32B-Instruct-GGUF:Q4_K_M" # does not work on my mac M3 Ultra - add_test_vision "ggml-org/Kimi-VL-A3B-Thinking-2506-GGUF:Q4_K_M" + # add_test_vision "ggml-org/Kimi-VL-A3B-Thinking-2506-GGUF:Q4_K_M" # not always working add_test_audio "ggml-org/ultravox-v0_5-llama-3_1-8b-GGUF:Q4_K_M" add_test_audio "ggml-org/Qwen2.5-Omni-7B-GGUF:Q4_K_M" @@ -122,21 +131,25 @@ for i in "${!arr_hf[@]}"; do bin="llama-mtmd-cli" prefix="${arr_prefix[$i]}" hf="${arr_hf[$i]}" - tmpl="${arr_tmpl[$i]}" + extra_args="${arr_extra_args[$i]}" inp_file="${arr_file[$i]}" echo "Running test with binary: $bin and HF model: $hf" echo "" echo "" - output=$(\ - "$PROJ_ROOT/build/bin/$bin" \ - -hf "$hf" \ - --image $SCRIPT_DIR/$inp_file \ - -p "what is the publisher name of the newspaper?" \ + cmd="$(printf %q "$PROJ_ROOT/build/bin/$bin") \ + -hf $(printf %q "$hf") \ + --image $(printf %q "$SCRIPT_DIR/$inp_file") \ --temp 0 -n 128 \ - ${tmpl:+--chat-template "$tmpl"} \ - 2>&1 | tee /dev/tty) + ${extra_args}" + + # if extra_args does not contain -p, we add a default prompt + if ! [[ "$extra_args" =~ "-p" ]]; then + cmd+=" -p \"what is the publisher name of the newspaper?\"" + fi + + output=$(eval "$cmd" 2>&1 | tee /dev/tty) echo "$output" > $SCRIPT_DIR/output/$bin-$(echo "$hf" | tr '/' '-').log @@ -144,9 +157,9 @@ for i in "${!arr_hf[@]}"; do if echo "$output" | grep -iq "new york" \ || (echo "$output" | grep -iq "men" && echo "$output" | grep -iq "walk") then - result="$prefix \033[32mOK\033[0m: $bin $hf" + result="$prefix \033[32mOK\033[0m: $hf" else - result="$prefix \033[31mFAIL\033[0m: $bin $hf" + result="$prefix \033[31mFAIL\033[0m: $hf" fi echo -e "$result" arr_res+=("$result") diff --git a/tools/perplexity/perplexity.cpp b/tools/perplexity/perplexity.cpp index caf080e8d1..1ead9c871e 100644 --- a/tools/perplexity/perplexity.cpp +++ b/tools/perplexity/perplexity.cpp @@ -2024,10 +2024,10 @@ int main(int argc, char ** argv) { llama_numa_init(params.numa); // load the model and apply lora adapter, if any - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); if (model == NULL) { LOG_ERR("%s: unable to load model\n", __func__); diff --git a/tools/quantize/tests.sh b/tools/quantize/tests.sh index acc54fd9b1..2cae588e9e 100644 --- a/tools/quantize/tests.sh +++ b/tools/quantize/tests.sh @@ -20,7 +20,7 @@ set -x SPLIT=$1/llama-gguf-split QUANTIZE=$1/llama-quantize -MAIN=$1/llama-cli +MAIN=$1/llama-completion WORK_PATH=$TMP_DIR/quantize ROOT_DIR=$(realpath $(dirname $0)/../../) diff --git a/tools/server/CMakeLists.txt b/tools/server/CMakeLists.txt index 1aa659a906..ae1a497be6 100644 --- a/tools/server/CMakeLists.txt +++ b/tools/server/CMakeLists.txt @@ -1,7 +1,33 @@ -set(TARGET llama-server) - include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR}) +# server-context containing the core server logic, used by llama-server and CLI + +set(TARGET server-context) + +add_library(${TARGET} STATIC + server-task.cpp + server-task.h + server-queue.cpp + server-queue.h + server-common.cpp + server-common.h + server-context.cpp + server-context.h +) + +if (BUILD_SHARED_LIBS) + set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON) +endif() + +target_include_directories(${TARGET} PRIVATE ../mtmd) +target_include_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR}) +target_link_libraries(${TARGET} PUBLIC common mtmd ${CMAKE_THREAD_LIBS_INIT}) + + +# llama-server executable + +set(TARGET llama-server) + if (NOT LLAMA_HTTPLIB) message(FATAL_ERROR "LLAMA_HTTPLIB is OFF, cannot build llama-server. Hint: to skip building server, set -DLLAMA_BUILD_SERVER=OFF") endif() @@ -43,7 +69,7 @@ install(TARGETS ${TARGET} RUNTIME) target_include_directories(${TARGET} PRIVATE ../mtmd) target_include_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR}) -target_link_libraries(${TARGET} PRIVATE common mtmd cpp-httplib ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE server-context PUBLIC common cpp-httplib ${CMAKE_THREAD_LIBS_INIT}) if (WIN32) TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32) diff --git a/tools/server/README.md b/tools/server/README.md index f98fb44c7b..073bcd2ccd 100644 --- a/tools/server/README.md +++ b/tools/server/README.md @@ -54,9 +54,8 @@ For the ful list of features, please refer to [server's changelog](https://githu | `--swa-full` | use full-size SWA cache (default: false)
[(more info)](https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
(env: LLAMA_ARG_SWA_FULL) | | `--kv-unified, -kvu` | use single unified KV buffer for the KV cache of all sequences (default: false)
[(more info)](https://github.com/ggml-org/llama.cpp/pull/14363)
(env: LLAMA_ARG_KV_UNIFIED) | | `-fa, --flash-attn [on\|off\|auto]` | set Flash Attention use ('on', 'off', or 'auto', default: 'auto')
(env: LLAMA_ARG_FLASH_ATTN) | -| `--no-perf` | disable internal libllama performance timings (default: false)
(env: LLAMA_ARG_NO_PERF) | -| `-e, --escape` | process escapes sequences (\n, \r, \t, \', \", \\) (default: true) | -| `--no-escape` | do not process escape sequences | +| `--perf, --no-perf` | whether to enable internal libllama performance timings (default: false)
(env: LLAMA_ARG_PERF) | +| `-e, --escape, --no-escape` | whether to process escapes sequences (\n, \r, \t, \', \", \\) (default: true) | | `--rope-scaling {none,linear,yarn}` | RoPE frequency scaling method, defaults to linear unless specified by the model
(env: LLAMA_ARG_ROPE_SCALING_TYPE) | | `--rope-scale N` | RoPE context scaling factor, expands context by a factor of N
(env: LLAMA_ARG_ROPE_SCALE) | | `--rope-freq-base N` | RoPE base frequency, used by NTK-aware scaling (default: loaded from model)
(env: LLAMA_ARG_ROPE_FREQ_BASE) | @@ -66,15 +65,15 @@ For the ful list of features, please refer to [server's changelog](https://githu | `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: -1.0)
(env: LLAMA_ARG_YARN_ATTN_FACTOR) | | `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: -1.0)
(env: LLAMA_ARG_YARN_BETA_SLOW) | | `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: -1.0)
(env: LLAMA_ARG_YARN_BETA_FAST) | -| `-nkvo, --no-kv-offload` | disable KV offload
(env: LLAMA_ARG_NO_KV_OFFLOAD) | -| `-nr, --no-repack` | disable weight repacking
(env: LLAMA_ARG_NO_REPACK) | -| `--no-host` | bypass host buffer allowing extra buffers to be used
(env: LLAMA_ARG_NO_HOST) | +| `-kvo, --kv-offload, -nkvo, --no-kv-offload` | whether to enable KV cache offloading (default: enabled)
(env: LLAMA_ARG_KV_OFFLOAD) | +| `--repack, -nr, --no-repack` | whether to enable weight repacking (default: enabled)
(env: LLAMA_ARG_REPACK) | +| `--no-host` | bypass host buffer allowing extra buffers to be used
(env: LLAMA_ARG_HOST) | | `-ctk, --cache-type-k TYPE` | KV cache data type for K
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_K) | | `-ctv, --cache-type-v TYPE` | KV cache data type for V
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V) | | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)
(env: LLAMA_ARG_DEFRAG_THOLD) | | `-np, --parallel N` | number of parallel sequences to decode (default: 1)
(env: LLAMA_ARG_N_PARALLEL) | | `--mlock` | force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | -| `--no-mmap` | do not memory-map model (slower load but may reduce pageouts if not using mlock)
(env: LLAMA_ARG_NO_MMAP) | +| `--mmap, --no-mmap` | whether to memory-map model (if disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)
(env: LLAMA_ARG_MMAP) | | `--numa TYPE` | attempt optimizations that help on some NUMA systems
- distribute: spread execution evenly over all nodes
- isolate: only spawn threads on CPUs on the node that execution started on
- numactl: use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggml-org/llama.cpp/issues/1437
(env: LLAMA_ARG_NUMA) | | `-dev, --device ` | comma-separated list of devices to use for offloading (none = don't offload)
use --list-devices to see a list of available devices
(env: LLAMA_ARG_DEVICE) | | `--list-devices` | print list of available devices and exit | @@ -87,7 +86,7 @@ For the ful list of features, please refer to [server's changelog](https://githu | `-mg, --main-gpu INDEX` | the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0)
(env: LLAMA_ARG_MAIN_GPU) | | `--check-tensors` | check model tensor data for invalid values (default: false) | | `--override-kv KEY=TYPE:VALUE` | advanced option to override model metadata by key. may be specified multiple times.
types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false | -| `--no-op-offload` | disable offloading host tensor operations to device (default: false) | +| `--op-offload, --no-op-offload` | whether to offload host tensor operations to device (default: true) | | `--lora FNAME` | path to LoRA adapter (can be repeated to use multiple adapters) | | `--lora-scaled FNAME SCALE` | path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters) | | `--control-vector FNAME` | add a control vector
note: this argument can be repeated to add multiple control vectors | @@ -157,19 +156,18 @@ For the ful list of features, please refer to [server's changelog](https://githu | -------- | ----------- | | `--ctx-checkpoints, --swa-checkpoints N` | max number of context checkpoints to create per slot (default: 8)
[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)
(env: LLAMA_ARG_CTX_CHECKPOINTS) | | `--cache-ram, -cram N` | set the maximum cache size in MiB (default: 8192, -1 - no limit, 0 - disable)
[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)
(env: LLAMA_ARG_CACHE_RAM) | -| `--no-context-shift` | disables context shift on infinite text generation (default: enabled)
(env: LLAMA_ARG_NO_CONTEXT_SHIFT) | -| `--context-shift` | enables context shift on infinite text generation (default: disabled)
(env: LLAMA_ARG_CONTEXT_SHIFT) | +| `--context-shift, --no-context-shift` | whether to use context shift on infinite text generation (default: disabled)
(env: LLAMA_ARG_CONTEXT_SHIFT) | | `-r, --reverse-prompt PROMPT` | halt generation at PROMPT, return control in interactive mode
| | `-sp, --special` | special tokens output enabled (default: false) | -| `--no-warmup` | skip warming up the model with an empty run | +| `--warmup, --no-warmup` | whether to perform warmup with an empty run (default: enabled) | | `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) | | `--pooling {none,mean,cls,last,rank}` | pooling type for embeddings, use model default if unspecified
(env: LLAMA_ARG_POOLING) | -| `-cb, --cont-batching` | enable continuous batching (a.k.a dynamic batching) (default: enabled)
(env: LLAMA_ARG_CONT_BATCHING) | -| `-nocb, --no-cont-batching` | disable continuous batching
(env: LLAMA_ARG_NO_CONT_BATCHING) | -| `--mmproj FILE` | path to a multimodal projector file. see tools/mtmd/README.md
note: if -hf is used, this argument can be omitted
(env: LLAMA_ARG_MMPROJ) | -| `--mmproj-url URL` | URL to a multimodal projector file. see tools/mtmd/README.md
(env: LLAMA_ARG_MMPROJ_URL) | -| `--no-mmproj` | explicitly disable multimodal projector, useful when using -hf
(env: LLAMA_ARG_NO_MMPROJ) | -| `--no-mmproj-offload` | do not offload multimodal projector to GPU
(env: LLAMA_ARG_NO_MMPROJ_OFFLOAD) | +| `-cb, --cont-batching, -nocb, --no-cont-batching` | whether to enable continuous batching (a.k.a dynamic batching) (default: enabled)
(env: LLAMA_ARG_CONT_BATCHING) | +| `-cb, --cont-batching, -nocb, --no-cont-batching` | whether to enable continuous batching (a.k.a dynamic batching) (default: enabled)
(env: LLAMA_ARG_CONT_BATCHING) | +| `-mm, --mmproj FILE` | path to a multimodal projector file. see tools/mtmd/README.md
note: if -hf is used, this argument can be omitted
(env: LLAMA_ARG_MMPROJ) | +| `-mmu, --mmproj-url URL` | URL to a multimodal projector file. see tools/mtmd/README.md
(env: LLAMA_ARG_MMPROJ_URL) | +| `--mmproj-auto, --no-mmproj, --no-mmproj-auto` | whether to use multimodal projector file (if available), useful when using -hf (default: enabled)
(env: LLAMA_ARG_MMPROJ_AUTO) | +| `--mmproj-offload, --no-mmproj-offload` | whether to enable GPU offloading for multimodal projector (default: enabled)
(env: LLAMA_ARG_MMPROJ_OFFLOAD) | | `--image-min-tokens N` | minimum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)
(env: LLAMA_ARG_IMAGE_MIN_TOKENS) | | `--image-max-tokens N` | maximum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)
(env: LLAMA_ARG_IMAGE_MAX_TOKENS) | | `--override-tensor-draft, -otd =,...` | override tensor buffer type for draft model | @@ -180,7 +178,7 @@ For the ful list of features, please refer to [server's changelog](https://githu | `--port PORT` | port to listen (default: 8080)
(env: LLAMA_ARG_PORT) | | `--path PATH` | path to serve static files from (default: )
(env: LLAMA_ARG_STATIC_PATH) | | `--api-prefix PREFIX` | prefix path the server serves from, without the trailing slash (default: )
(env: LLAMA_ARG_API_PREFIX) | -| `--no-webui` | Disable the Web UI (default: enabled)
(env: LLAMA_ARG_NO_WEBUI) | +| `--webui, --no-webui` | whether to enable the Web UI (default: enabled)
(env: LLAMA_ARG_WEBUI) | | `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)
(env: LLAMA_ARG_EMBEDDINGS) | | `--reranking, --rerank` | enable reranking endpoint on server (default: disabled)
(env: LLAMA_ARG_RERANKING) | | `--api-key KEY` | API key to use for authentication (default: none)
(env: LLAMA_API_KEY) | @@ -193,20 +191,19 @@ For the ful list of features, please refer to [server's changelog](https://githu | `--cache-reuse N` | min chunk size to attempt reusing from the cache via KV shifting (default: 0)
[(card)](https://ggml.ai/f0.png)
(env: LLAMA_ARG_CACHE_REUSE) | | `--metrics` | enable prometheus compatible metrics endpoint (default: disabled)
(env: LLAMA_ARG_ENDPOINT_METRICS) | | `--props` | enable changing global properties via POST /props (default: disabled)
(env: LLAMA_ARG_ENDPOINT_PROPS) | -| `--slots` | enable slots monitoring endpoint (default: enabled)
(env: LLAMA_ARG_ENDPOINT_SLOTS) | -| `--no-slots` | disables slots monitoring endpoint
(env: LLAMA_ARG_NO_ENDPOINT_SLOTS) | +| `--slots, --no-slots` | expose slots monitoring endpoint (default: enabled)
(env: LLAMA_ARG_ENDPOINT_SLOTS) | | `--slot-save-path PATH` | path to save slot kv cache (default: disabled) | +| `--media-path PATH` | directory for loading local media files; files can be accessed via file:// URLs using relative paths (default: disabled) | | `--models-dir PATH` | directory containing models for the router server (default: disabled)
(env: LLAMA_ARG_MODELS_DIR) | +| `--models-preset PATH` | path to INI file containing model presets for the router server (default: disabled)
(env: LLAMA_ARG_MODELS_PRESET) | | `--models-max N` | for router server, maximum number of models to load simultaneously (default: 4, 0 = unlimited)
(env: LLAMA_ARG_MODELS_MAX) | -| `--models-allow-extra-args` | for router server, allow extra arguments for models; important: some arguments can allow users to access local file system, use with caution (default: disabled)
(env: LLAMA_ARG_MODELS_ALLOW_EXTRA_ARGS) | -| `--no-models-autoload` | disables automatic loading of models (default: enabled)
(env: LLAMA_ARG_NO_MODELS_AUTOLOAD) | -| `--jinja` | use jinja template for chat (default: enabled)

(env: LLAMA_ARG_JINJA) | -| `--no-jinja` | disable jinja template for chat (default: enabled)

(env: LLAMA_ARG_NO_JINJA) | +| `--models-autoload, --no-models-autoload` | for router server, whether to automatically load models (default: enabled)
(env: LLAMA_ARG_MODELS_AUTOLOAD) | +| `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: enabled)
(env: LLAMA_ARG_JINJA) | | `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:
- none: leaves thoughts unparsed in `message.content`
- deepseek: puts thoughts in `message.reasoning_content`
- deepseek-legacy: keeps `` tags in `message.content` while also populating `message.reasoning_content`
(default: auto)
(env: LLAMA_ARG_THINK) | | `--reasoning-budget N` | controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)
(env: LLAMA_ARG_THINK_BUDGET) | | `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | | `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) | -| `--no-prefill-assistant` | whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)
when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled

(env: LLAMA_ARG_NO_PREFILL_ASSISTANT) | +| `--prefill-assistant, --no-prefill-assistant` | whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)
when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled

(env: LLAMA_ARG_PREFILL_ASSISTANT) | | `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.10, 0.0 = disabled)
| | `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) | | `-td, --threads-draft N` | number of threads to use during generation (default: same as --threads) | @@ -236,6 +233,11 @@ For the ful list of features, please refer to [server's changelog](https://githu Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var. +For boolean options like `--mmap` or `--kv-offload`, the environment variable is handled as shown in this example: +- `LLAMA_ARG_MMAP=true` means enabled, other accepted values are: `1`, `on`, `enabled` +- `LLAMA_ARG_MMAP=false` means disabled, other accepted values are: `0`, `off`, `disabled` +- If `LLAMA_ARG_NO_MMAP` is present (no matter the value), it means disabling mmap + Example usage of docker compose with environment variables: ```yml @@ -1369,6 +1371,11 @@ llama-server ### Model sources +There are 3 possible sources for model files: +1. Cached models (controlled by the `LLAMA_CACHE` environment variable) +2. Custom model directory (set via the `--models-dir` argument) +3. Custom preset (set via the `--models-preset` argument) + By default, the router looks for models in the cache. You can add Hugging Face models to the cache with: ```sh @@ -1413,6 +1420,51 @@ llama-server -ctx 8192 -n 1024 -np 2 Note: model instances inherit both command line arguments and environment variables from the router server. +Alternatively, you can also add GGUF based preset (see next section) + +### Model presets + +Model presets allow advanced users to define custom configurations using an `.ini` file: + +```sh +llama-server --models-preset ./my-models.ini +``` + +Each section in the file defines a new preset. Keys within a section correspond to command-line arguments (without leading dashes). For example, the argument `--n-gpu-layer 123` is written as `n-gpu-layer = 123`. + +Short argument forms (e.g., `c`, `ngl`) and environment variable names (e.g., `LLAMA_ARG_N_GPU_LAYERS`) are also supported as keys. + +Example: + +```ini +version = 1 + +; If the key corresponds to an existing model on the server, +; this will be used as the default config for that model +[ggml-org/MY-MODEL-GGUF:Q8_0] +; string value +chat-template = chatml +; numeric value +n-gpu-layer = 123 +; flag value (for certain flags, you need to use the "no-" prefix for negation) +jinja = true +; shorthand argument (for example, context size) +c = 4096 +; environment variable name +LLAMA_ARG_CACHE_RAM = 0 +; file paths are relative to server's CWD +model-draft = ./my-models/draft.gguf +; but it's RECOMMENDED to use absolute path +model-draft = /Users/abc/my-models/draft.gguf + +; If the key does NOT correspond to an existing model, +; you need to specify at least the model path +[custom_model] +model = /Users/abc/my-awesome-model-Q4_K_M.gguf +``` + +Note: some arguments are controlled by router (e.g., host, port, API key, HF repo, model alias). They will be removed or overwritten upload loading. + ### Routing requests Requests are routed according to the requested model name. diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz index 2db04e9522..036feff1c3 100644 Binary files a/tools/server/public/index.html.gz and b/tools/server/public/index.html.gz differ diff --git a/tools/server/server-common.cpp b/tools/server/server-common.cpp index b403864e0e..ab6b3aa7ce 100644 --- a/tools/server/server-common.cpp +++ b/tools/server/server-common.cpp @@ -972,6 +972,9 @@ json oaicompat_chat_params_parse( inputs.parallel_tool_calls = json_value(body, "parallel_tool_calls", false); inputs.add_generation_prompt = json_value(body, "add_generation_prompt", true); inputs.reasoning_format = opt.reasoning_format; + if (body.contains("reasoning_format")) { + inputs.reasoning_format = common_reasoning_format_from_name(body.at("reasoning_format").get()); + } inputs.enable_thinking = opt.enable_thinking; if (!inputs.tools.empty() && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) { if (body.contains("grammar")) { diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index 4578f8d7a9..90898b5ec4 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -153,7 +153,7 @@ struct server_slot { // sampling json json_schema; - struct common_sampler * smpl = nullptr; + common_sampler_ptr smpl; llama_token sampled; // in speculative mode, this is the last accepted token llama_tokens drafted; @@ -510,8 +510,8 @@ struct server_context_impl { common_params params_base; // note: keep these alive - they determine the lifetime of the model, context, etc. - common_init_result llama_init; - common_init_result llama_init_dft; + common_init_result_ptr llama_init; + common_init_result_ptr llama_init_dft; llama_model * model = nullptr; llama_context * ctx = nullptr; @@ -557,9 +557,6 @@ struct server_context_impl { // Clear any sampling context for (server_slot & slot : slots) { - common_sampler_free(slot.smpl); - slot.smpl = nullptr; - llama_free(slot.ctx_dft); slot.ctx_dft = nullptr; @@ -580,8 +577,8 @@ struct server_context_impl { llama_init = common_init_from_params(params_base); - model = llama_init.model.get(); - ctx = llama_init.context.get(); + model = llama_init->model(); + ctx = llama_init->context(); if (model == nullptr) { SRV_ERR("failed to load model, '%s'\n", params_base.model.path.c_str()); @@ -613,25 +610,25 @@ struct server_context_impl { llama_init_dft = common_init_from_params(params_dft); - model_dft = llama_init_dft.model.get(); + model_dft = llama_init_dft->model(); if (model_dft == nullptr) { SRV_ERR("failed to load draft model, '%s'\n", params_base.speculative.model.path.c_str()); return false; } - vocab_dft_compatible = common_speculative_are_compatible(ctx, llama_init_dft.context.get()); + vocab_dft_compatible = common_speculative_are_compatible(ctx, llama_init_dft->context()); if (!vocab_dft_compatible) { SRV_INF("the draft model '%s' is not compatible with the target model '%s'. tokens will be translated between the draft and target models.\n", params_base.speculative.model.path.c_str(), params_base.model.path.c_str()); } - const int n_ctx_dft = llama_n_ctx(llama_init_dft.context.get()); + const int n_ctx_dft = llama_n_ctx(llama_init_dft->context()); cparams_dft = common_context_params_to_llama(params_dft); cparams_dft.n_batch = n_ctx_dft; // the context is not needed - we will create one for each slot - llama_init_dft.context.reset(); + llama_init_dft->free_context(); } chat_templates = common_chat_templates_init(model, params_base.chat_template); @@ -1051,18 +1048,15 @@ struct server_context_impl { // initialize samplers { - if (slot.smpl != nullptr) { - common_sampler_free(slot.smpl); - } + slot.smpl.reset(common_sampler_init(model, task.params.sampling)); - slot.smpl = common_sampler_init(model, task.params.sampling); if (slot.smpl == nullptr) { // for now, the only error that may happen here is invalid grammar send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST); return false; } - SLT_INF(slot, "sampler chain: %s\n", common_sampler_print(slot.smpl).c_str()); + SLT_INF(slot, "sampler chain: %s\n", common_sampler_print(slot.smpl.get()).c_str()); } // initialize draft batch @@ -1216,11 +1210,10 @@ struct server_context_impl { } void populate_token_probs(const server_slot & slot, completion_token_output & result, bool post_sampling, bool special, int idx) const { - size_t n_probs = slot.task->params.sampling.n_probs; - size_t n_vocab = llama_vocab_n_tokens(vocab); + const size_t n_probs = slot.task->params.sampling.n_probs; if (post_sampling) { - const auto * cur_p = common_sampler_get_candidates(slot.smpl, true); + const auto * cur_p = common_sampler_get_candidates(slot.smpl.get(), true); const size_t max_probs = cur_p->size; // set probability for sampled token @@ -1245,7 +1238,7 @@ struct server_context_impl { std::vector cur = get_token_probabilities(ctx, idx); // set probability for sampled token - for (size_t i = 0; i < n_vocab; i++) { + for (size_t i = 0; i < cur.size(); i++) { // set probability for sampled token if (cur[i].id == result.tok) { result.prob = cur[i].p; @@ -1255,7 +1248,7 @@ struct server_context_impl { // set probability for top n_probs tokens result.probs.reserve(n_probs); - for (size_t i = 0; i < std::min(n_vocab, n_probs); i++) { + for (size_t i = 0; i < std::min(cur.size(), n_probs); i++) { result.probs.push_back({ cur[i].id, common_token_to_piece(ctx, cur[i].id, special), @@ -1474,6 +1467,44 @@ struct server_context_impl { // Functions to process the task // + // tokenize the input if it's set by CLI, return false on error + bool tokenize_cli_input(server_task & task) { + if (task.cli_input == nullptr) { + return true; // nothing to do + } + try { + auto & opt = oai_parser_opt; + common_chat_templates_inputs inputs; + inputs.messages = common_chat_msgs_parse_oaicompat(task.cli_input); + inputs.tools = {}; // TODO + inputs.tool_choice = COMMON_CHAT_TOOL_CHOICE_NONE; + inputs.json_schema = ""; // TODO + inputs.grammar = ""; // TODO + inputs.use_jinja = opt.use_jinja; + inputs.parallel_tool_calls = false; + inputs.add_generation_prompt = true; + inputs.reasoning_format = opt.reasoning_format; + inputs.enable_thinking = opt.enable_thinking; + + // Apply chat template to the list of messages + auto chat_params = common_chat_templates_apply(opt.tmpls, inputs); + + // tokenize the resulting prompt + auto & prompt = chat_params.prompt; + if (mctx != nullptr) { + task.tokens = process_mtmd_prompt(mctx, prompt, task.cli_files); + } else { + task.tokens = std::move(tokenize_input_prompts(vocab, mctx, prompt, true, true)[0]); + } + task.cli_input.clear(); + task.cli_files.clear(); + } catch (const std::exception & e) { + send_error(task, std::string("Failed to format input: ") + e.what(), ERROR_TYPE_INVALID_REQUEST); + return false; + } + return true; + } + void process_single_task(server_task && task) { switch (task.type) { case SERVER_TASK_TYPE_COMPLETION: @@ -1481,6 +1512,10 @@ struct server_context_impl { case SERVER_TASK_TYPE_EMBEDDING: case SERVER_TASK_TYPE_RERANK: { + if (!tokenize_cli_input(task)) { + break; + } + const int id_slot = task.id_slot; server_slot * slot = id_slot != -1 ? get_slot_by_id(id_slot) : get_available_slot(task); @@ -1690,7 +1725,6 @@ struct server_context_impl { res->id = task.id; queue_results.send(std::move(res)); } break; - } } @@ -2260,13 +2294,13 @@ struct server_context_impl { GGML_ASSERT(batch.n_tokens > 0); - common_sampler_reset(slot.smpl); + common_sampler_reset(slot.smpl.get()); // Process all prompt tokens through sampler system for (int i = 0; i < slot.task->n_tokens(); ++i) { llama_token id = input_tokens[i]; if (id != LLAMA_TOKEN_NULL) { - common_sampler_accept(slot.smpl, id, false); + common_sampler_accept(slot.smpl.get(), id, false); } } @@ -2484,11 +2518,11 @@ struct server_context_impl { const int tok_idx = slot.i_batch - i; - llama_token id = common_sampler_sample(slot.smpl, ctx, tok_idx); + llama_token id = common_sampler_sample(slot.smpl.get(), ctx, tok_idx); slot.i_batch = -1; - common_sampler_accept(slot.smpl, id, true); + common_sampler_accept(slot.smpl.get(), id, true); slot.n_decoded += 1; @@ -2529,7 +2563,7 @@ struct server_context_impl { size_t n_draft = slot.drafted.size(); // the accepted tokens from the speculation - const auto ids = common_sampler_sample_and_accept_n(slot.smpl, ctx, slot.i_batch_dft, slot.drafted); + const auto ids = common_sampler_sample_and_accept_n(slot.smpl.get(), ctx, slot.i_batch_dft, slot.drafted); slot.i_batch_dft.clear(); slot.drafted.clear(); @@ -2626,6 +2660,15 @@ server_response_reader server_context::get_response_reader() { return impl->get_response_reader(); } +server_context_info server_context::get_info() const { + return server_context_info { + /* build_info */ build_info, + /* model_name */ impl->model_name, + /* has_inp_image */ impl->oai_parser_opt.allow_image, + /* has_inp_audio */ impl->oai_parser_opt.allow_audio, + }; +} + // generator-like API for HTTP response generation diff --git a/tools/server/server-context.h b/tools/server/server-context.h index eaa1380877..230b25952e 100644 --- a/tools/server/server-context.h +++ b/tools/server/server-context.h @@ -9,6 +9,13 @@ struct server_context_impl; // private implementation +struct server_context_info { + std::string build_info; + std::string model_name; + bool has_inp_image; + bool has_inp_audio; +}; + struct server_context { std::unique_ptr impl; @@ -33,6 +40,10 @@ struct server_context { // get a new response reader, used by CLI application server_response_reader get_response_reader(); + + // get server info + // used by CLI application + server_context_info get_info() const; }; diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp index 6f88e93c4b..3690c0bb82 100644 --- a/tools/server/server-models.cpp +++ b/tools/server/server-models.cpp @@ -1,6 +1,7 @@ #include "server-common.h" #include "server-models.h" +#include "preset.h" #include "download.h" #include // TODO: remove this once we use HTTP client from download.h @@ -15,6 +16,7 @@ #include #include #include +#include #ifdef _WIN32 #include @@ -33,6 +35,10 @@ #define CMD_EXIT "exit" +// address for child process, this is needed because router may run on 0.0.0.0 +// ref: https://github.com/ggml-org/llama.cpp/issues/17862 +#define CHILD_ADDR "127.0.0.1" + static std::filesystem::path get_server_exec_path() { #if defined(_WIN32) wchar_t buf[32768] = { 0 }; // Large buffer to handle long paths @@ -132,6 +138,93 @@ static std::vector list_local_models(const std::string & dir) { return models; } +// +// server_presets +// + + +server_presets::server_presets(int argc, char ** argv, common_params & base_params, const std::string & presets_path) + : ctx_params(common_params_parser_init(base_params, LLAMA_EXAMPLE_SERVER)) { + if (!presets_path.empty()) { + presets = common_presets_load(presets_path, ctx_params); + SRV_INF("Loaded %zu presets from %s\n", presets.size(), presets_path.c_str()); + } + + // populate reserved args (will be appended by the router) + for (auto & opt : ctx_params.options) { + if (opt.env == nullptr) { + continue; + } + std::string env = opt.env; + if (env == "LLAMA_ARG_PORT" || + env == "LLAMA_ARG_HOST" || + env == "LLAMA_ARG_ALIAS" || + env == "LLAMA_ARG_API_KEY" || + env == "LLAMA_ARG_MODELS_DIR" || + env == "LLAMA_ARG_MODELS_MAX" || + env == "LLAMA_ARG_MODELS_PRESET" || + env == "LLAMA_ARG_MODEL" || + env == "LLAMA_ARG_MMPROJ" || + env == "LLAMA_ARG_HF_REPO" || + env == "LLAMA_ARG_NO_MODELS_AUTOLOAD") { + control_args[env] = opt; + } + } + + // read base args from router's argv + common_params_to_map(argc, argv, LLAMA_EXAMPLE_SERVER, base_args); + + // remove any router-controlled args from base_args + for (const auto & cargs : control_args) { + auto it = base_args.find(cargs.second); + if (it != base_args.end()) { + base_args.erase(it); + } + } +} + +common_preset server_presets::get_preset(const std::string & name) { + auto it = presets.find(name); + if (it != presets.end()) { + return it->second; + } + return common_preset(); +} + +void server_presets::render_args(server_model_meta & meta) { + common_preset preset = meta.preset; // copy + // merging 3 kinds of args: + // 1. model-specific args (from preset) + // force removing control args if any + for (auto & cargs : control_args) { + if (preset.options.find(cargs.second) != preset.options.end()) { + SRV_WRN("Preset '%s' contains reserved arg '%s', removing it\n", preset.name.c_str(), cargs.second.args[0]); + preset.options.erase(cargs.second); + } + } + // 2. base args (from router) + // inherit from base args + for (const auto & [arg, value] : base_args) { + preset.options[arg] = value; + } + // 3. control args (from router) + // set control values + preset.options[control_args["LLAMA_ARG_HOST"]] = CHILD_ADDR; + preset.options[control_args["LLAMA_ARG_PORT"]] = std::to_string(meta.port); + preset.options[control_args["LLAMA_ARG_ALIAS"]] = meta.name; + if (meta.in_cache) { + preset.options[control_args["LLAMA_ARG_HF_REPO"]] = meta.name; + } else { + preset.options[control_args["LLAMA_ARG_MODEL"]] = meta.path; + if (!meta.path_mmproj.empty()) { + preset.options[control_args["LLAMA_ARG_MMPROJ"]] = meta.path_mmproj; + } + } + meta.args = preset.to_args(); + // add back the binary path at the front + meta.args.insert(meta.args.begin(), get_server_exec_path().string()); +} + // // server_models // @@ -140,7 +233,7 @@ server_models::server_models( const common_params & params, int argc, char ** argv, - char ** envp) : base_params(params) { + char ** envp) : base_params(params), presets(argc, argv, base_params, params.models_preset) { for (int i = 0; i < argc; i++) { base_args.push_back(std::string(argv[i])); } @@ -155,11 +248,58 @@ server_models::server_models( LOG_WRN("failed to get server executable path: %s\n", e.what()); LOG_WRN("using original argv[0] as fallback: %s\n", base_args[0].c_str()); } - // TODO: allow refreshing cached model list - // add cached models + load_models(); +} + +void server_models::add_model(server_model_meta && meta) { + if (mapping.find(meta.name) != mapping.end()) { + throw std::runtime_error(string_format("model '%s' appears multiple times", meta.name.c_str())); + } + presets.render_args(meta); // populate meta.args + std::string name = meta.name; + mapping[name] = instance_t{ + /* subproc */ std::make_shared(), + /* th */ std::thread(), + /* meta */ std::move(meta) + }; +} + +static std::vector list_custom_path_models(server_presets & presets) { + // detect any custom-path models in presets + std::vector custom_models; + for (auto & [model_name, preset] : presets.presets) { + local_model model; + model.name = model_name; + std::vector to_erase; + for (auto & [arg, value] : preset.options) { + std::string env(arg.env ? arg.env : ""); + if (env == "LLAMA_ARG_MODEL") { + model.path = value; + to_erase.push_back(arg); + } + if (env == "LLAMA_ARG_MMPROJ") { + model.path_mmproj = value; + to_erase.push_back(arg); + } + } + for (auto & arg : to_erase) { + preset.options.erase(arg); + } + if (!model.name.empty() && !model.path.empty()) { + custom_models.push_back(model); + } + } + return custom_models; +} + +// TODO: allow refreshing cached model list +void server_models::load_models() { + // loading models from 3 sources: + // 1. cached models auto cached_models = common_list_cached_models(); for (const auto & model : cached_models) { server_model_meta meta{ + /* preset */ presets.get_preset(model.to_string()), /* name */ model.to_string(), /* path */ model.manifest_path, /* path_mmproj */ "", // auto-detected when loading @@ -170,21 +310,18 @@ server_models::server_models( /* args */ std::vector(), /* exit_code */ 0 }; - mapping[meta.name] = instance_t{ - /* subproc */ std::make_shared(), - /* th */ std::thread(), - /* meta */ meta - }; + add_model(std::move(meta)); } - // add local models specificed via --models-dir - if (!params.models_dir.empty()) { - auto local_models = list_local_models(params.models_dir); + // 2. local models specificed via --models-dir + if (!base_params.models_dir.empty()) { + auto local_models = list_local_models(base_params.models_dir); for (const auto & model : local_models) { if (mapping.find(model.name) != mapping.end()) { // already exists in cached models, skip continue; } server_model_meta meta{ + /* preset */ presets.get_preset(model.name), /* name */ model.name, /* path */ model.path, /* path_mmproj */ model.path_mmproj, @@ -195,13 +332,31 @@ server_models::server_models( /* args */ std::vector(), /* exit_code */ 0 }; - mapping[meta.name] = instance_t{ - /* subproc */ std::make_shared(), - /* th */ std::thread(), - /* meta */ meta - }; + add_model(std::move(meta)); } } + // 3. custom-path models specified in presets + auto custom_models = list_custom_path_models(presets); + for (const auto & model : custom_models) { + server_model_meta meta{ + /* preset */ presets.get_preset(model.name), + /* name */ model.name, + /* path */ model.path, + /* path_mmproj */ model.path_mmproj, + /* in_cache */ false, + /* port */ 0, + /* status */ SERVER_MODEL_STATUS_UNLOADED, + /* last_used */ 0, + /* args */ std::vector(), + /* exit_code */ 0 + }; + add_model(std::move(meta)); + } + // log available models + SRV_INF("Available models (%zu) (*: custom preset)\n", mapping.size()); + for (const auto & [name, inst] : mapping) { + SRV_INF(" %c %s\n", inst.meta.preset.name.empty() ? ' ' : '*', name.c_str()); + } } void server_models::update_meta(const std::string & name, const server_model_meta & meta) { @@ -335,19 +490,7 @@ void server_models::unload_lru() { } } -static void add_or_replace_arg(std::vector & args, const std::string & key, const std::string & value) { - for (size_t i = 0; i < args.size(); i++) { - if (args[i] == key && i + 1 < args.size()) { - args[i + 1] = value; - return; - } - } - // not found, append - args.push_back(key); - args.push_back(value); -} - -void server_models::load(const std::string & name, bool auto_load) { +void server_models::load(const std::string & name) { if (!has_model(name)) { throw std::runtime_error("model name=" + name + " is not found"); } @@ -376,26 +519,10 @@ void server_models::load(const std::string & name, bool auto_load) { { SRV_INF("spawning server instance with name=%s on port %d\n", inst.meta.name.c_str(), inst.meta.port); - std::vector child_args; - if (auto_load && !meta.args.empty()) { - child_args = meta.args; // copy previous args - } else { - child_args = base_args; // copy - if (inst.meta.in_cache) { - add_or_replace_arg(child_args, "-hf", inst.meta.name); - } else { - add_or_replace_arg(child_args, "-m", inst.meta.path); - if (!inst.meta.path_mmproj.empty()) { - add_or_replace_arg(child_args, "--mmproj", inst.meta.path_mmproj); - } - } - } + presets.render_args(inst.meta); // update meta.args - // set model args - add_or_replace_arg(child_args, "--port", std::to_string(inst.meta.port)); - add_or_replace_arg(child_args, "--alias", inst.meta.name); - - std::vector child_env = base_env; // copy + std::vector child_args = inst.meta.args; // copy + std::vector child_env = base_env; // copy child_env.push_back("LLAMA_SERVER_ROUTER_PORT=" + std::to_string(base_params.port)); SRV_INF("%s", "spawning server instance with args:\n"); @@ -541,7 +668,7 @@ bool server_models::ensure_model_loaded(const std::string & name) { } if (meta->status == SERVER_MODEL_STATUS_UNLOADED) { SRV_INF("model name=%s is not loaded, loading...\n", name.c_str()); - load(name, true); + load(name); } SRV_INF("waiting until model name=%s is fully loaded...\n", name.c_str()); @@ -571,7 +698,7 @@ server_http_res_ptr server_models::proxy_request(const server_http_req & req, co SRV_INF("proxying request to model %s on port %d\n", name.c_str(), meta->port); auto proxy = std::make_unique( method, - base_params.hostname, + CHILD_ADDR, meta->port, req.path, req.headers, @@ -724,38 +851,6 @@ void server_models_routes::init_routes() { return models.proxy_request(req, method, name, true); // update last usage for POST request only }; - this->get_router_models = [this](const server_http_req &) { - auto res = std::make_unique(); - json models_json = json::array(); - auto all_models = models.get_all_meta(); - std::time_t t = std::time(0); - for (const auto & meta : all_models) { - json status { - {"value", server_model_status_to_string(meta.status)}, - {"args", meta.args}, - }; - if (meta.is_failed()) { - status["exit_code"] = meta.exit_code; - status["failed"] = true; - } - models_json.push_back(json { - {"id", meta.name}, - {"object", "model"}, // for OAI-compat - {"owned_by", "llamacpp"}, // for OAI-compat - {"created", t}, // for OAI-compat - {"in_cache", meta.in_cache}, - {"path", meta.path}, - {"status", status}, - // TODO: add other fields, may require reading GGUF metadata - }); - } - res_ok(res, { - {"data", models_json}, - {"object", "list"}, - }); - return res; - }; - this->post_router_models_load = [this](const server_http_req & req) { auto res = std::make_unique(); json body = json::parse(req.body); @@ -769,7 +864,7 @@ void server_models_routes::init_routes() { res_err(res, format_error_response("model is already loaded", ERROR_TYPE_INVALID_REQUEST)); return res; } - models.load(name, false); + models.load(name); res_ok(res, {{"success", true}}); return res; }; @@ -793,9 +888,12 @@ void server_models_routes::init_routes() { std::time_t t = std::time(0); for (const auto & meta : all_models) { json status { - {"value", server_model_status_to_string(meta.status)}, - {"args", meta.args}, + {"value", server_model_status_to_string(meta.status)}, + {"args", meta.args}, }; + if (!meta.preset.name.empty()) { + status["preset"] = meta.preset.to_ini(); + } if (meta.is_failed()) { status["exit_code"] = meta.exit_code; status["failed"] = true; diff --git a/tools/server/server-models.h b/tools/server/server-models.h index 526e7488dc..9cdbbad9b6 100644 --- a/tools/server/server-models.h +++ b/tools/server/server-models.h @@ -1,6 +1,7 @@ #pragma once #include "common.h" +#include "preset.h" #include "server-http.h" #include @@ -47,6 +48,7 @@ static std::string server_model_status_to_string(server_model_status status) { } struct server_model_meta { + common_preset preset; std::string name; std::string path; std::string path_mmproj; // only available if in_cache=false @@ -54,7 +56,7 @@ struct server_model_meta { int port = 0; server_model_status status = SERVER_MODEL_STATUS_UNLOADED; int64_t last_used = 0; // for LRU unloading - std::vector args; // additional args passed to the model instance (used for debugging) + std::vector args; // args passed to the model instance, will be populated by render_args() int exit_code = 0; // exit code of the model instance process (only valid if status == FAILED) bool is_active() const { @@ -66,6 +68,19 @@ struct server_model_meta { } }; +// the server_presets struct holds the presets read from presets.ini +// as well as base args from the router server +struct server_presets { + common_presets presets; + common_params_context ctx_params; + std::map base_args; + std::map control_args; // args reserved for server control + + server_presets(int argc, char ** argv, common_params & base_params, const std::string & models_dir); + common_preset get_preset(const std::string & name); + void render_args(server_model_meta & meta); +}; + struct subprocess_s; struct server_models { @@ -85,14 +100,21 @@ private: std::vector base_args; std::vector base_env; + server_presets presets; + void update_meta(const std::string & name, const server_model_meta & meta); // unload least recently used models if the limit is reached void unload_lru(); + // not thread-safe, caller must hold mutex + void add_model(server_model_meta && meta); + public: server_models(const common_params & params, int argc, char ** argv, char ** envp); + void load_models(); + // check if a model instance exists bool has_model(const std::string & name); @@ -102,8 +124,7 @@ public: // return a copy of all model metadata std::vector get_all_meta(); - // if auto_load is true, load the model with previous args if any - void load(const std::string & name, bool auto_load); + void load(const std::string & name); void unload(const std::string & name); void unload_all(); diff --git a/tools/server/server-queue.h b/tools/server/server-queue.h index 726eadf4ef..8780d7fe12 100644 --- a/tools/server/server-queue.h +++ b/tools/server/server-queue.h @@ -135,7 +135,10 @@ struct server_response_reader { stop(); } - void post_task(server_task && tasks); + int get_new_id() { + return queue_tasks.get_new_id(); + } + void post_task(server_task && task); void post_tasks(std::vector && tasks); bool has_next() const; diff --git a/tools/server/server-task.h b/tools/server/server-task.h index 9011ff944b..0759094a01 100644 --- a/tools/server/server-task.h +++ b/tools/server/server-task.h @@ -120,6 +120,10 @@ struct server_task { task_params params; server_tokens tokens; + // only used by CLI, this delegates the tokenization to the server + json cli_input = nullptr; + std::vector cli_files; + server_task_type type; // used by SERVER_TASK_TYPE_SLOT_SAVE, SERVER_TASK_TYPE_SLOT_RESTORE, SERVER_TASK_TYPE_SLOT_ERASE diff --git a/tools/server/tests/unit/test_compat_anthropic.py b/tools/server/tests/unit/test_compat_anthropic.py index d55dd1d945..e0a003557e 100644 --- a/tools/server/tests/unit/test_compat_anthropic.py +++ b/tools/server/tests/unit/test_compat_anthropic.py @@ -684,7 +684,7 @@ def test_anthropic_streaming_content_block_indices(): # Request that might produce both text and tool use res = server.make_stream_request("POST", "/v1/messages", data={ "model": "test", - "max_tokens": 200, + "max_tokens": 400, "stream": True, "tools": [{ "name": "test_tool", diff --git a/tools/server/webui/package-lock.json b/tools/server/webui/package-lock.json index 9c1c2499cf..4f37b308b1 100644 --- a/tools/server/webui/package-lock.json +++ b/tools/server/webui/package-lock.json @@ -41,7 +41,7 @@ "@tailwindcss/vite": "^4.0.0", "@types/node": "^22", "@vitest/browser": "^3.2.3", - "bits-ui": "^2.8.11", + "bits-ui": "^2.14.4", "clsx": "^2.1.1", "dexie": "^4.0.11", "eslint": "^9.18.0", @@ -3343,17 +3343,17 @@ } }, "node_modules/bits-ui": { - "version": "2.8.11", - "resolved": "https://registry.npmjs.org/bits-ui/-/bits-ui-2.8.11.tgz", - "integrity": "sha512-lKN9rAk69my6j7H1D4B87r8LrHuEtfEsf1xCixBj9yViql2BdI3f04HyyyT7T1GOCpgb9+8b0B+nm3LN81Konw==", + "version": "2.14.4", + "resolved": "https://registry.npmjs.org/bits-ui/-/bits-ui-2.14.4.tgz", + "integrity": "sha512-W6kenhnbd/YVvur+DKkaVJ6GldE53eLewur5AhUCqslYQ0vjZr8eWlOfwZnMiPB+PF5HMVqf61vXBvmyrAmPWg==", "dev": true, "license": "MIT", "dependencies": { "@floating-ui/core": "^1.7.1", "@floating-ui/dom": "^1.7.1", "esm-env": "^1.1.2", - "runed": "^0.29.1", - "svelte-toolbelt": "^0.9.3", + "runed": "^0.35.1", + "svelte-toolbelt": "^0.10.6", "tabbable": "^6.2.0" }, "engines": { @@ -3368,9 +3368,9 @@ } }, "node_modules/bits-ui/node_modules/runed": { - "version": "0.29.2", - "resolved": "https://registry.npmjs.org/runed/-/runed-0.29.2.tgz", - "integrity": "sha512-0cq6cA6sYGZwl/FvVqjx9YN+1xEBu9sDDyuWdDW1yWX7JF2wmvmVKfH+hVCZs+csW+P3ARH92MjI3H9QTagOQA==", + "version": "0.35.1", + "resolved": "https://registry.npmjs.org/runed/-/runed-0.35.1.tgz", + "integrity": "sha512-2F4Q/FZzbeJTFdIS/PuOoPRSm92sA2LhzTnv6FXhCoENb3huf5+fDuNOg1LNvGOouy3u/225qxmuJvcV3IZK5Q==", "dev": true, "funding": [ "https://github.com/sponsors/huntabyte", @@ -3378,23 +3378,31 @@ ], "license": "MIT", "dependencies": { - "esm-env": "^1.0.0" + "dequal": "^2.0.3", + "esm-env": "^1.0.0", + "lz-string": "^1.5.0" }, "peerDependencies": { + "@sveltejs/kit": "^2.21.0", "svelte": "^5.7.0" + }, + "peerDependenciesMeta": { + "@sveltejs/kit": { + "optional": true + } } }, "node_modules/bits-ui/node_modules/svelte-toolbelt": { - "version": "0.9.3", - "resolved": "https://registry.npmjs.org/svelte-toolbelt/-/svelte-toolbelt-0.9.3.tgz", - "integrity": "sha512-HCSWxCtVmv+c6g1ACb8LTwHVbDqLKJvHpo6J8TaqwUme2hj9ATJCpjCPNISR1OCq2Q4U1KT41if9ON0isINQZw==", + "version": "0.10.6", + "resolved": "https://registry.npmjs.org/svelte-toolbelt/-/svelte-toolbelt-0.10.6.tgz", + "integrity": "sha512-YWuX+RE+CnWYx09yseAe4ZVMM7e7GRFZM6OYWpBKOb++s+SQ8RBIMMe+Bs/CznBMc0QPLjr+vDBxTAkozXsFXQ==", "dev": true, "funding": [ "https://github.com/sponsors/huntabyte" ], "dependencies": { "clsx": "^2.1.1", - "runed": "^0.29.0", + "runed": "^0.35.1", "style-to-object": "^1.0.8" }, "engines": { diff --git a/tools/server/webui/package.json b/tools/server/webui/package.json index 987a7239ed..c20ab3cfde 100644 --- a/tools/server/webui/package.json +++ b/tools/server/webui/package.json @@ -43,7 +43,7 @@ "@tailwindcss/vite": "^4.0.0", "@types/node": "^22", "@vitest/browser": "^3.2.3", - "bits-ui": "^2.8.11", + "bits-ui": "^2.14.4", "clsx": "^2.1.1", "dexie": "^4.0.11", "eslint": "^9.18.0", diff --git a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentThumbnailFile.svelte b/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentThumbnailFile.svelte index 6fdd857214..908db5894b 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentThumbnailFile.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentThumbnailFile.svelte @@ -1,6 +1,6 @@
@@ -229,6 +264,25 @@
{/if} + +
+

Delete All Conversations

+ +

+ Permanently delete all conversations and their messages. This action cannot be undone. + Consider exporting your conversations first if you want to keep a backup. +

+ + +
@@ -249,3 +303,15 @@ onCancel={() => (showImportDialog = false)} onConfirm={handleImportConfirm} /> + + diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebar.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebar.svelte index 1d313e284e..aa0c27f6d3 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebar.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebar.svelte @@ -9,6 +9,7 @@ import Input from '$lib/components/ui/input/input.svelte'; import { conversationsStore, conversations } from '$lib/stores/conversations.svelte'; import { chatStore } from '$lib/stores/chat.svelte'; + import { getPreviewText } from '$lib/utils/text'; import ChatSidebarActions from './ChatSidebarActions.svelte'; const sidebar = Sidebar.useSidebar(); @@ -20,6 +21,9 @@ let showEditDialog = $state(false); let selectedConversation = $state(null); let editedName = $state(''); + let selectedConversationNamePreview = $derived.by(() => + selectedConversation ? getPreviewText(selectedConversation.name) : '' + ); let filteredConversations = $derived.by(() => { if (searchQuery.trim().length > 0) { @@ -162,7 +166,7 @@ bind:open={showDeleteDialog} title="Delete Conversation" description={selectedConversation - ? `Are you sure you want to delete "${selectedConversation.name}"? This action cannot be undone and will permanently remove all messages in this conversation.` + ? `Are you sure you want to delete "${selectedConversationNamePreview}"? This action cannot be undone and will permanently remove all messages in this conversation.` : ''} confirmText="Delete" cancelText="Cancel" diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarSearch.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarSearch.svelte index c9e6c6616a..afc9847028 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarSearch.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarSearch.svelte @@ -1,6 +1,5 @@ -
- - - -
+ diff --git a/tools/server/webui/src/lib/components/app/index.ts b/tools/server/webui/src/lib/components/app/index.ts index 87b24598b7..8631d4fb3b 100644 --- a/tools/server/webui/src/lib/components/app/index.ts +++ b/tools/server/webui/src/lib/components/app/index.ts @@ -64,6 +64,7 @@ export { default as CopyToClipboardIcon } from './misc/CopyToClipboardIcon.svelt export { default as KeyboardShortcutInfo } from './misc/KeyboardShortcutInfo.svelte'; export { default as MarkdownContent } from './misc/MarkdownContent.svelte'; export { default as RemoveButton } from './misc/RemoveButton.svelte'; +export { default as SearchInput } from './misc/SearchInput.svelte'; export { default as SyntaxHighlightedCode } from './misc/SyntaxHighlightedCode.svelte'; export { default as ModelsSelector } from './models/ModelsSelector.svelte'; diff --git a/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte b/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte index 9c37bde0d4..2a4a39535e 100644 --- a/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte +++ b/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte @@ -504,6 +504,14 @@ background: hsl(var(--muted) / 0.1); } + /* User message markdown should keep table borders visible on light primary backgrounds */ + div.markdown-user-content :global(table), + div.markdown-user-content :global(th), + div.markdown-user-content :global(td), + div.markdown-user-content :global(.table-wrapper) { + border-color: currentColor; + } + /* Horizontal rules */ div :global(hr) { border: none; @@ -642,6 +650,21 @@ background: var(--muted); } + /* Disable hover effects when rendering user messages */ + .markdown-user-content :global(a), + .markdown-user-content :global(a:hover) { + color: var(--primary-foreground); + } + + .markdown-user-content :global(table:hover) { + box-shadow: none; + } + + .markdown-user-content :global(th:hover), + .markdown-user-content :global(td:hover) { + background: inherit; + } + /* Enhanced blockquotes */ div :global(blockquote) { transition: all 0.2s ease; diff --git a/tools/server/webui/src/lib/components/app/misc/SearchInput.svelte b/tools/server/webui/src/lib/components/app/misc/SearchInput.svelte new file mode 100644 index 0000000000..15cd6abaa9 --- /dev/null +++ b/tools/server/webui/src/lib/components/app/misc/SearchInput.svelte @@ -0,0 +1,73 @@ + + +
+ + + + + {#if showClearButton} + + {/if} +
diff --git a/tools/server/webui/src/lib/components/app/models/ModelsSelector.svelte b/tools/server/webui/src/lib/components/app/models/ModelsSelector.svelte index c4331e92f1..ac0937696d 100644 --- a/tools/server/webui/src/lib/components/app/models/ModelsSelector.svelte +++ b/tools/server/webui/src/lib/components/app/models/ModelsSelector.svelte @@ -2,8 +2,8 @@ import { onMount, tick } from 'svelte'; import { ChevronDown, EyeOff, Loader2, MicOff, Package, Power } from '@lucide/svelte'; import * as Tooltip from '$lib/components/ui/tooltip'; + import * as Popover from '$lib/components/ui/popover'; import { cn } from '$lib/components/ui/utils'; - import { portalToBody } from '$lib/utils'; import { modelsStore, modelOptions, @@ -17,12 +17,8 @@ import { usedModalities, conversationsStore } from '$lib/stores/conversations.svelte'; import { ServerModelStatus } from '$lib/enums'; import { isRouterMode } from '$lib/stores/server.svelte'; - import { DialogModelInformation } from '$lib/components/app'; - import { - MENU_MAX_WIDTH, - MENU_OFFSET, - VIEWPORT_GUTTER - } from '$lib/constants/floating-ui-constraints'; + import { DialogModelInformation, SearchInput } from '$lib/components/app'; + import type { ModelOption } from '$lib/types/models'; interface Props { class?: string; @@ -145,185 +141,126 @@ return options.some((option) => option.model === currentModel); }); - let isOpen = $state(false); - let showModelDialog = $state(false); - let container: HTMLDivElement | null = null; - let menuRef = $state(null); - let triggerButton = $state(null); - let menuPosition = $state<{ - top: number; - left: number; - width: number; - placement: 'top' | 'bottom'; - maxHeight: number; - } | null>(null); + let searchTerm = $state(''); + let searchInputRef = $state(null); + let highlightedIndex = $state(-1); - onMount(async () => { - try { - await modelsStore.fetch(); - } catch (error) { - console.error('Unable to load models:', error); - } + let filteredOptions: ModelOption[] = $derived( + (() => { + const term = searchTerm.trim().toLowerCase(); + if (!term) return options; + + return options.filter( + (option) => + option.model.toLowerCase().includes(term) || option.name?.toLowerCase().includes(term) + ); + })() + ); + + // Get indices of compatible options for keyboard navigation + let compatibleIndices = $derived( + filteredOptions + .map((option, index) => (isModelCompatible(option) ? index : -1)) + .filter((i) => i !== -1) + ); + + // Reset highlighted index when search term changes + $effect(() => { + void searchTerm; + highlightedIndex = -1; }); - function toggleOpen() { + let isOpen = $state(false); + let showModelDialog = $state(false); + + onMount(() => { + modelsStore.fetch().catch((error) => { + console.error('Unable to load models:', error); + }); + }); + + function handleOpenChange(open: boolean) { if (loading || updating) return; - if (isRouter) { - // Router mode: show dropdown - if (isOpen) { - closeMenu(); - } else { - openMenu(); + if (open) { + isOpen = true; + searchTerm = ''; + highlightedIndex = -1; + + // Focus search input after popover opens + tick().then(() => { + requestAnimationFrame(() => searchInputRef?.focus()); + }); + + if (isRouter) { + modelsStore.fetchRouterModels().then(() => { + modelsStore.fetchModalitiesForLoadedModels(); + }); } } else { - // Single model mode: show dialog - showModelDialog = true; + isOpen = false; + searchTerm = ''; + highlightedIndex = -1; } } - async function openMenu() { + function handleTriggerClick() { if (loading || updating) return; - isOpen = true; - await tick(); - updateMenuPosition(); - requestAnimationFrame(() => updateMenuPosition()); - - if (isRouter) { - modelsStore.fetchRouterModels().then(() => { - modelsStore.fetchModalitiesForLoadedModels(); - }); + if (!isRouter) { + // Single model mode: show dialog instead of popover + showModelDialog = true; } + // For router mode, the Popover handles open/close } export function open() { if (isRouter) { - openMenu(); + handleOpenChange(true); } else { showModelDialog = true; } } function closeMenu() { - if (!isOpen) return; - - isOpen = false; - menuPosition = null; + handleOpenChange(false); } - function handlePointerDown(event: PointerEvent) { - if (!container) return; + function handleSearchKeyDown(event: KeyboardEvent) { + if (event.isComposing) return; - const target = event.target as Node | null; + if (event.key === 'ArrowDown') { + event.preventDefault(); + if (compatibleIndices.length === 0) return; - if (target && !container.contains(target) && !(menuRef && menuRef.contains(target))) { - closeMenu(); - } - } - - function handleKeydown(event: KeyboardEvent) { - if (event.key === 'Escape') { - closeMenu(); - } - } - - function handleResize() { - if (isOpen) { - updateMenuPosition(); - } - } - - function updateMenuPosition() { - if (!isOpen || !triggerButton || !menuRef) return; - - const triggerRect = triggerButton.getBoundingClientRect(); - const viewportWidth = window.innerWidth; - const viewportHeight = window.innerHeight; - - if (viewportWidth === 0 || viewportHeight === 0) return; - - const scrollWidth = menuRef.scrollWidth; - const scrollHeight = menuRef.scrollHeight; - - const availableWidth = Math.max(0, viewportWidth - VIEWPORT_GUTTER * 2); - const constrainedMaxWidth = Math.min(MENU_MAX_WIDTH, availableWidth || MENU_MAX_WIDTH); - const safeMaxWidth = - constrainedMaxWidth > 0 ? constrainedMaxWidth : Math.min(MENU_MAX_WIDTH, viewportWidth); - const desiredMinWidth = Math.min(160, safeMaxWidth || 160); - - let width = Math.min( - Math.max(triggerRect.width, scrollWidth, desiredMinWidth), - safeMaxWidth || 320 - ); - - const availableBelow = Math.max( - 0, - viewportHeight - VIEWPORT_GUTTER - triggerRect.bottom - MENU_OFFSET - ); - const availableAbove = Math.max(0, triggerRect.top - VIEWPORT_GUTTER - MENU_OFFSET); - const viewportAllowance = Math.max(0, viewportHeight - VIEWPORT_GUTTER * 2); - const fallbackAllowance = Math.max(1, viewportAllowance > 0 ? viewportAllowance : scrollHeight); - - function computePlacement(placement: 'top' | 'bottom') { - const available = placement === 'bottom' ? availableBelow : availableAbove; - const allowedHeight = - available > 0 ? Math.min(available, fallbackAllowance) : fallbackAllowance; - const maxHeight = Math.min(scrollHeight, allowedHeight); - const height = Math.max(0, maxHeight); - - let top: number; - if (placement === 'bottom') { - const rawTop = triggerRect.bottom + MENU_OFFSET; - const minTop = VIEWPORT_GUTTER; - const maxTop = viewportHeight - VIEWPORT_GUTTER - height; - if (maxTop < minTop) { - top = minTop; - } else { - top = Math.min(Math.max(rawTop, minTop), maxTop); - } + const currentPos = compatibleIndices.indexOf(highlightedIndex); + if (currentPos === -1 || currentPos === compatibleIndices.length - 1) { + highlightedIndex = compatibleIndices[0]; } else { - const rawTop = triggerRect.top - MENU_OFFSET - height; - const minTop = VIEWPORT_GUTTER; - const maxTop = viewportHeight - VIEWPORT_GUTTER - height; - if (maxTop < minTop) { - top = minTop; - } else { - top = Math.max(Math.min(rawTop, maxTop), minTop); + highlightedIndex = compatibleIndices[currentPos + 1]; + } + } else if (event.key === 'ArrowUp') { + event.preventDefault(); + if (compatibleIndices.length === 0) return; + + const currentPos = compatibleIndices.indexOf(highlightedIndex); + if (currentPos === -1 || currentPos === 0) { + highlightedIndex = compatibleIndices[compatibleIndices.length - 1]; + } else { + highlightedIndex = compatibleIndices[currentPos - 1]; + } + } else if (event.key === 'Enter') { + event.preventDefault(); + if (highlightedIndex >= 0 && highlightedIndex < filteredOptions.length) { + const option = filteredOptions[highlightedIndex]; + if (isModelCompatible(option)) { + handleSelect(option.id); } - } - - return { placement, top, height, maxHeight }; - } - - const belowMetrics = computePlacement('bottom'); - const aboveMetrics = computePlacement('top'); - - let metrics = belowMetrics; - if (scrollHeight > belowMetrics.maxHeight && aboveMetrics.maxHeight > belowMetrics.maxHeight) { - metrics = aboveMetrics; - } - - let left = triggerRect.right - width; - const maxLeft = viewportWidth - VIEWPORT_GUTTER - width; - if (maxLeft < VIEWPORT_GUTTER) { - left = VIEWPORT_GUTTER; - } else { - if (left > maxLeft) { - left = maxLeft; - } - if (left < VIEWPORT_GUTTER) { - left = VIEWPORT_GUTTER; + } else if (compatibleIndices.length > 0) { + // No selection - highlight first compatible option + highlightedIndex = compatibleIndices[0]; } } - - menuPosition = { - top: Math.round(metrics.top), - left: Math.round(left), - width: Math.round(width), - placement: metrics.placement, - maxHeight: Math.round(metrics.maxHeight) - }; } async function handleSelect(modelId: string) { @@ -356,6 +293,14 @@ if (shouldCloseMenu) { closeMenu(); + + // Focus the chat textarea after model selection + requestAnimationFrame(() => { + const textarea = document.querySelector( + '[data-slot="chat-form"] textarea' + ); + textarea?.focus(); + }); } } @@ -404,10 +349,7 @@ } - - - -
+
{#if loading && options.length === 0 && isRouter}
@@ -418,9 +360,8 @@ {:else} {@const selectedOption = getDisplayOption()} -
- + - {#if isOpen && isRouter} -
+ +
0 - ? `${menuPosition.maxHeight}px` - : undefined} + class="order-1 shrink-0 border-b p-4 group-data-[side=top]/popover-content:order-2 group-data-[side=top]/popover-content:border-t group-data-[side=top]/popover-content:border-b-0" + > + +
+
{#if !isCurrentModelInCache() && currentModel}
- {/if} -
+ + {/if}
diff --git a/tools/server/webui/src/lib/components/ui/popover/index.ts b/tools/server/webui/src/lib/components/ui/popover/index.ts new file mode 100644 index 0000000000..c5937fb3a0 --- /dev/null +++ b/tools/server/webui/src/lib/components/ui/popover/index.ts @@ -0,0 +1,19 @@ +import Root from './popover.svelte'; +import Close from './popover-close.svelte'; +import Content from './popover-content.svelte'; +import Trigger from './popover-trigger.svelte'; +import Portal from './popover-portal.svelte'; + +export { + Root, + Content, + Trigger, + Close, + Portal, + // + Root as Popover, + Content as PopoverContent, + Trigger as PopoverTrigger, + Close as PopoverClose, + Portal as PopoverPortal +}; diff --git a/tools/server/webui/src/lib/components/ui/popover/popover-close.svelte b/tools/server/webui/src/lib/components/ui/popover/popover-close.svelte new file mode 100644 index 0000000000..dc4dec4b33 --- /dev/null +++ b/tools/server/webui/src/lib/components/ui/popover/popover-close.svelte @@ -0,0 +1,7 @@ + + + diff --git a/tools/server/webui/src/lib/components/ui/popover/popover-content.svelte b/tools/server/webui/src/lib/components/ui/popover/popover-content.svelte new file mode 100644 index 0000000000..2d3513d347 --- /dev/null +++ b/tools/server/webui/src/lib/components/ui/popover/popover-content.svelte @@ -0,0 +1,37 @@ + + + + + diff --git a/tools/server/webui/src/lib/components/ui/popover/popover-portal.svelte b/tools/server/webui/src/lib/components/ui/popover/popover-portal.svelte new file mode 100644 index 0000000000..25efb877b7 --- /dev/null +++ b/tools/server/webui/src/lib/components/ui/popover/popover-portal.svelte @@ -0,0 +1,7 @@ + + + diff --git a/tools/server/webui/src/lib/components/ui/popover/popover-trigger.svelte b/tools/server/webui/src/lib/components/ui/popover/popover-trigger.svelte new file mode 100644 index 0000000000..5ef3d0e932 --- /dev/null +++ b/tools/server/webui/src/lib/components/ui/popover/popover-trigger.svelte @@ -0,0 +1,17 @@ + + + diff --git a/tools/server/webui/src/lib/components/ui/popover/popover.svelte b/tools/server/webui/src/lib/components/ui/popover/popover.svelte new file mode 100644 index 0000000000..f39b867a69 --- /dev/null +++ b/tools/server/webui/src/lib/components/ui/popover/popover.svelte @@ -0,0 +1,7 @@ + + + diff --git a/tools/server/webui/src/lib/constants/floating-ui-constraints.ts b/tools/server/webui/src/lib/constants/floating-ui-constraints.ts index c95d3f1841..003fc77acb 100644 --- a/tools/server/webui/src/lib/constants/floating-ui-constraints.ts +++ b/tools/server/webui/src/lib/constants/floating-ui-constraints.ts @@ -1,3 +1,2 @@ export const VIEWPORT_GUTTER = 8; export const MENU_OFFSET = 6; -export const MENU_MAX_WIDTH = 320; diff --git a/tools/server/webui/src/lib/stores/conversations.svelte.ts b/tools/server/webui/src/lib/stores/conversations.svelte.ts index f766561971..3300eb3113 100644 --- a/tools/server/webui/src/lib/stores/conversations.svelte.ts +++ b/tools/server/webui/src/lib/stores/conversations.svelte.ts @@ -385,8 +385,7 @@ class ConversationsStore { this.conversations = this.conversations.filter((c) => c.id !== convId); if (this.activeConversation?.id === convId) { - this.activeConversation = null; - this.activeMessages = []; + this.clearActiveConversation(); await goto(`?new_chat=true#/`); } } catch (error) { @@ -394,6 +393,29 @@ class ConversationsStore { } } + /** + * Deletes all conversations and their messages + */ + async deleteAll(): Promise { + try { + const allConversations = await DatabaseService.getAllConversations(); + + for (const conv of allConversations) { + await DatabaseService.deleteConversation(conv.id); + } + + this.clearActiveConversation(); + this.conversations = []; + + toast.success('All conversations deleted'); + + await goto(`?new_chat=true#/`); + } catch (error) { + console.error('Failed to delete all conversations:', error); + toast.error('Failed to delete conversations'); + } + } + // ───────────────────────────────────────────────────────────────────────────── // Import/Export // ───────────────────────────────────────────────────────────────────────────── diff --git a/tools/server/webui/src/lib/stores/models.svelte.ts b/tools/server/webui/src/lib/stores/models.svelte.ts index 29416c2fe5..34b26403e4 100644 --- a/tools/server/webui/src/lib/stores/models.svelte.ts +++ b/tools/server/webui/src/lib/stores/models.svelte.ts @@ -295,14 +295,21 @@ class ModelsStore { * Fetch props for a specific model from /props endpoint * Uses caching to avoid redundant requests * + * In ROUTER mode, this will only fetch props if the model is loaded, + * since unloaded models return 400 from /props endpoint. + * * @param modelId - Model identifier to fetch props for - * @returns Props data or null if fetch failed + * @returns Props data or null if fetch failed or model not loaded */ async fetchModelProps(modelId: string): Promise { // Return cached props if available const cached = this.modelPropsCache.get(modelId); if (cached) return cached; + if (serverStore.isRouterMode && !this.isModelLoaded(modelId)) { + return null; + } + // Avoid duplicate fetches if (this.modelPropsFetching.has(modelId)) return null; diff --git a/tools/server/webui/src/lib/utils/file-preview.ts b/tools/server/webui/src/lib/utils/file-preview.ts index 115f8727a9..26a60533ae 100644 --- a/tools/server/webui/src/lib/utils/file-preview.ts +++ b/tools/server/webui/src/lib/utils/file-preview.ts @@ -34,12 +34,3 @@ export function getFileTypeLabel(input: string | undefined): string { // Handle AttachmentType or other plain strings return input.toUpperCase(); } - -/** - * Truncates text content for preview display - * @param content - The text content to truncate - * @returns Truncated content with ellipsis if needed - */ -export function getPreviewText(content: string): string { - return content.length > 150 ? content.substring(0, 150) + '...' : content; -} diff --git a/tools/server/webui/src/lib/utils/index.ts b/tools/server/webui/src/lib/utils/index.ts index d8a893ed64..2485e34de3 100644 --- a/tools/server/webui/src/lib/utils/index.ts +++ b/tools/server/webui/src/lib/utils/index.ts @@ -43,7 +43,8 @@ export { createMessageCountMap, getMessageCount } from './conversation-utils'; export { copyToClipboard, copyCodeToClipboard } from './copy'; // File preview utilities -export { getFileTypeLabel, getPreviewText } from './file-preview'; +export { getFileTypeLabel } from './file-preview'; +export { getPreviewText } from './text'; // File type utilities export { diff --git a/tools/server/webui/src/lib/utils/latex-protection.test.ts b/tools/server/webui/src/lib/utils/latex-protection.test.ts index 2354f8fa0e..40fe1b0db2 100644 --- a/tools/server/webui/src/lib/utils/latex-protection.test.ts +++ b/tools/server/webui/src/lib/utils/latex-protection.test.ts @@ -303,6 +303,27 @@ $$\n\\pi_n(\\mathbb{S}^3) = \\begin{cases} expect(output).toBe(input); // Code blocks prevent misinterpretation }); + test('preserves backslash parentheses in code blocks (GitHub issue)', () => { + const input = '```python\nfoo = "\\(bar\\)"\n```'; + const output = preprocessLaTeX(input); + + expect(output).toBe(input); // Code blocks should not have LaTeX conversion applied + }); + + test('preserves backslash brackets in code blocks', () => { + const input = '```python\nfoo = "\\[bar\\]"\n```'; + const output = preprocessLaTeX(input); + + expect(output).toBe(input); // Code blocks should not have LaTeX conversion applied + }); + + test('preserves backslash parentheses in inline code', () => { + const input = 'Use `foo = "\\(bar\\)"` in your code.'; + const output = preprocessLaTeX(input); + + expect(output).toBe(input); + }); + test('escape backslash in mchem ce', () => { const input = 'mchem ce:\n$\\ce{2H2(g) + O2(g) -> 2H2O(l)}$'; const output = preprocessLaTeX(input); diff --git a/tools/server/webui/src/lib/utils/latex-protection.ts b/tools/server/webui/src/lib/utils/latex-protection.ts index 7f5cf2cddf..cafa2d4761 100644 --- a/tools/server/webui/src/lib/utils/latex-protection.ts +++ b/tools/server/webui/src/lib/utils/latex-protection.ts @@ -226,19 +226,16 @@ export function preprocessLaTeX(content: string): string { return expr; }); - // Step 5: Restore code blocks - content = content.replace(/<>/g, (_, index) => { - return codeBlocks[parseInt(index)]; - }); - - // Step 6: Apply additional escaping functions (brackets and mhchem) + // Step 5: Apply additional escaping functions (brackets and mhchem) + // This must happen BEFORE restoring code blocks to avoid affecting code content content = escapeBrackets(content); if (doEscapeMhchem && (content.includes('\\ce{') || content.includes('\\pu{'))) { content = escapeMhchem(content); } - // Final pass: Convert \(...\) → $...$, \[...\] → $$...$$ + // Step 6: Convert remaining \(...\) → $...$, \[...\] → $$...$$ + // This must happen BEFORE restoring code blocks to avoid affecting code content content = content // Using the look‑behind pattern `(? { - return `${prefix}$$${content}$$`; + (_, content: string) => { + return `$$${content}$$`; } ); - // Step 7: Restore blockquote markers + // Step 7: Restore code blocks + // This happens AFTER all LaTeX conversions to preserve code content + content = content.replace(/<>/g, (_, index) => { + return codeBlocks[parseInt(index)]; + }); + + // Step 8: Restore blockquote markers if (blockquoteMarkers.size > 0) { const finalLines = content.split('\n'); const restoredLines = finalLines.map((line, index) => { diff --git a/tools/server/webui/src/lib/utils/text.ts b/tools/server/webui/src/lib/utils/text.ts new file mode 100644 index 0000000000..5c5dd0fe8c --- /dev/null +++ b/tools/server/webui/src/lib/utils/text.ts @@ -0,0 +1,7 @@ +/** + * Returns a shortened preview of the provided content capped at the given length. + * Appends an ellipsis when the content exceeds the maximum. + */ +export function getPreviewText(content: string, max = 150): string { + return content.length > max ? content.slice(0, max) + '...' : content; +} diff --git a/tools/tts/tts.cpp b/tools/tts/tts.cpp index eaf56591d9..8c39fce8ba 100644 --- a/tools/tts/tts.cpp +++ b/tools/tts/tts.cpp @@ -568,10 +568,10 @@ int main(int argc, char ** argv) { llama_context * ctx_ttc = NULL; llama_context * ctx_cts = NULL; - common_init_result llama_init_ttc = common_init_from_params(params); + auto llama_init_ttc = common_init_from_params(params); - model_ttc = llama_init_ttc.model.get(); - ctx_ttc = llama_init_ttc.context.get(); + model_ttc = llama_init_ttc->model(); + ctx_ttc = llama_init_ttc->context(); if (model_ttc == nullptr || ctx_ttc == nullptr) { return ENOENT; @@ -583,10 +583,10 @@ int main(int argc, char ** argv) { params.embedding = true; params.n_ubatch = params.n_batch; - common_init_result llama_init_cts = common_init_from_params(params); + auto llama_init_cts = common_init_from_params(params); - model_cts = llama_init_cts.model.get(); - ctx_cts = llama_init_cts.context.get(); + model_cts = llama_init_cts->model(); + ctx_cts = llama_init_cts->context(); if (model_cts == nullptr || ctx_cts == nullptr) { return ENOENT; diff --git a/vendor/cpp-httplib/CMakeLists.txt b/vendor/cpp-httplib/CMakeLists.txt index 369502d7ae..8f0d15d1fd 100644 --- a/vendor/cpp-httplib/CMakeLists.txt +++ b/vendor/cpp-httplib/CMakeLists.txt @@ -9,6 +9,11 @@ if (NOT MSVC) endif() target_link_libraries (${TARGET} PRIVATE Threads::Threads) + +if (WIN32 AND NOT MSVC) + target_link_libraries(${TARGET} PRIVATE ws2_32) +endif() + target_compile_features(${TARGET} PRIVATE cxx_std_17) target_compile_definitions(${TARGET} PRIVATE